! kaggle competitions files home-credit-default-risk


!pip install kaggle

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!pwd

/content


!pwd

/content


!ls -l kaggle.json

-rw-r--r-- 1 root root 69 Apr 26 01:44 kaggle.json


!mkdir ~/.kaggle
!cp kaggle.json ~/.kaggle
!chmod 600 ~/.kaggle/kaggle.json


! kaggle competitions files home-credit-default-risk

name                                 size  creationDate         
----------------------------------  -----  -------------------  
installments_payments.csv           690MB  2019-12-11 02:55:35  
bureau_balance.csv                  358MB  2019-12-11 02:55:35  
bureau.csv                          162MB  2019-12-11 02:55:35  
application_test.csv                 25MB  2019-12-11 02:55:35  
HomeCredit_columns_description.csv   37KB  2019-12-11 02:55:35  
POS_CASH_balance.csv                375MB  2019-12-11 02:55:35  
application_train.csv               158MB  2019-12-11 02:55:35  
credit_card_balance.csv             405MB  2019-12-11 02:55:35  
sample_submission.csv               524KB  2019-12-11 02:55:35  
previous_application.csv            386MB  2019-12-11 02:55:35

name                       [  rows cols]     MegaBytes         
-----------------------  ------------------  -------
application_train       : [  307,511, 122]:   158MB
application_test        : [   48,744, 121]:   25MB
bureau                  : [ 1,716,428, 17]    162MB
bureau_balance          : [ 27,299,925, 3]:   358MB
credit_card_balance     : [  3,840,312, 23]   405MB
installments_payments   : [ 13,605,401, 8]    690MB
previous_application    : [  1,670,214, 37]   386MB
POS_CASH_balance        : [ 10,001,358, 8]    375MB

DATA_DIR = "../../../Data/home-credit-default-risk"   #same level as course repo in the data directory


DATA_DIR = "../data/home-credit-default-risk"   #same level as course repo in the data directory
#DATA_DIR = os.path.join('./ddddd/')
!mkdir ../data ../data/home-credit-default-risk


!ls -l {DATA_DIR}

total 0


! kaggle competitions download home-credit-default-risk -p $DATA_DIR

Downloading home-credit-default-risk.zip to ../data/home-credit-default-risk
100% 688M/688M [00:23<00:00, 34.1MB/s]
100% 688M/688M [00:23<00:00, 31.1MB/s]


!pwd

/content


!ls -l $DATA_DIR

total 704708
-rw-r--r-- 1 root root 721616255 Apr 26 01:45 home-credit-default-risk.zip


#!rm -r  DATA_DIR


import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')


unzippingReq = True #True
if unzippingReq: #please modify this code 
    zip_ref = zipfile.ZipFile(f'{DATA_DIR}/home-credit-default-risk.zip', 'r')
    # extractall():  Extract all members from the archive to the current working directory. path specifies a different directory to extract to
    zip_ref.extractall('../data/home-credit-default-risk') 
    zip_ref.close()


ls -l ../data/home-credit-default-risk/application_train.csv/

ls: cannot access '../data/home-credit-default-risk/application_train.csv/': Not a directory


import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')

def load_data(in_path, name):
    df = pd.read_csv(in_path)
    print(f"{name}: shape is {df.shape}")
    print(df.info())
    display(df.head(5))
    return df

datasets={}  # lets store the datasets in a dictionary so we can keep track of them easily
ds_name = 'application_train'
DATA_DIR=f"../data/home-credit-default-risk/"
pd.set_option('display.max_columns', None)
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

datasets['application_train'].shape

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

(307511, 122)


#COLUMNS PRESENT IN THE DATA
print(datasets['application_train'].columns)

Index(['SK_ID_CURR', 'TARGET', 'NAME_CONTRACT_TYPE', 'CODE_GENDER',
       'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'CNT_CHILDREN', 'AMT_INCOME_TOTAL',
       'AMT_CREDIT', 'AMT_ANNUITY',
       ...
       'FLAG_DOCUMENT_18', 'FLAG_DOCUMENT_19', 'FLAG_DOCUMENT_20',
       'FLAG_DOCUMENT_21', 'AMT_REQ_CREDIT_BUREAU_HOUR',
       'AMT_REQ_CREDIT_BUREAU_DAY', 'AMT_REQ_CREDIT_BUREAU_WEEK',
       'AMT_REQ_CREDIT_BUREAU_MON', 'AMT_REQ_CREDIT_BUREAU_QRT',
       'AMT_REQ_CREDIT_BUREAU_YEAR'],
      dtype='object', length=122)


DATA_DIR

'../data/home-credit-default-risk/'


ds_name = 'application_test'
datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None


%%time
ds_names = ("application_train", "application_test", "bureau","bureau_balance","credit_card_balance","installments_payments",
            "previous_application","POS_CASH_balance")

for ds_name in ds_names:
    datasets[ds_name] = load_data(os.path.join(DATA_DIR, f'{ds_name}.csv'), ds_name)

application_train: shape is (307511, 122)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB
None

application_test: shape is (48744, 121)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48744 entries, 0 to 48743
Columns: 121 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(40), object(16)
memory usage: 45.0+ MB
None

bureau: shape is (1716428, 17)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB
None

bureau_balance: shape is (27299925, 3)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB
None

credit_card_balance: shape is (3840312, 23)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB
None

installments_payments: shape is (13605401, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB
None

previous_application: shape is (1670214, 37)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB
None

POS_CASH_balance: shape is (10001358, 8)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 int64  
 7   SK_DPD_DEF             int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB
None

CPU times: user 35 s, sys: 7.56 s, total: 42.5 s
Wall time: 43.4 s


for ds_name in datasets.keys():
    print(f'dataset {ds_name:24}: [ {datasets[ds_name].shape[0]:10,}, {datasets[ds_name].shape[1]}]')

dataset application_train       : [    307,511, 122]
dataset application_test        : [     48,744, 121]
dataset bureau                  : [  1,716,428, 17]
dataset bureau_balance          : [ 27,299,925, 3]
dataset credit_card_balance     : [  3,840,312, 23]
dataset installments_payments   : [ 13,605,401, 8]
dataset previous_application    : [  1,670,214, 37]
dataset POS_CASH_balance        : [ 10,001,358, 8]


# COLUMNS INFO
datasets["application_train"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 307511 entries, 0 to 307510
Columns: 122 entries, SK_ID_CURR to AMT_REQ_CREDIT_BUREAU_YEAR
dtypes: float64(65), int64(41), object(16)
memory usage: 286.2+ MB


datasets["application_train"].describe() #numerical only features


datasets["application_test"].describe() #numerical only features


datasets["application_train"].describe(include='all') #look at all categorical and numerical


percent = (datasets["application_train"].isnull().sum()/datasets["application_train"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_train"].isna().sum().sort_values(ascending = False)
missing_application_train_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Train Missing Count"])
missing_application_train_data.head(20)


missinga5 = missing_application_train_data.loc[missing_application_train_data['Percent'] > 55]
missinga5


drop = datasets["application_train"]['NONLIVINGAPARTMENTS_MODE'].corr(datasets["application_train"]['TARGET'])
drop

-0.0015565608459037438


drop = datasets["application_train"]['NONLIVINGAPARTMENTS_AVG'].corr(datasets["application_train"]['TARGET'])
drop

-0.0031761075207261394


drop = datasets["application_train"]['NONLIVINGAPARTMENTS_MEDI'].corr(datasets["application_train"]['TARGET'])
drop

-0.0027571486021083823


import seaborn as sns
import matplotlib.pyplot as plt

# Filter rows with count of missing values greater than 0
missing_application_train_data = missing_application_train_data[missing_application_train_data['Train Missing Count'] > 0]
print("Number of columns with missing value greater than 0:",len(missing_application_train_data))
missing_more_than_60=missing_application_train_data[missing_application_train_data['Percent'] >=60]
print("Number of columns with more than 60% missing values:", len(missing_more_than_60))

plt.figure(figsize=(15, 25))

sns.set_style("whitegrid")
ax = sns.barplot(x="Train Missing Count", y=missing_application_train_data.index, data=missing_application_train_data,
                 palette="Blues_d")

# Set the figure size and axis labels

ax.set_xlabel("Number of Missing Values")
ax.set_ylabel("Features")

plt.title('Missing values in Train columns')

# Add values to the bars
for i, v in enumerate(missing_application_train_data['Train Missing Count']):
    ax.text(v + 10, i + .25, str(v), color='black', fontweight='bold')

# Show the plot
plt.show()

Number of columns with missing value greater than 0: 67
Number of columns with more than 60% missing values: 17


percent = (datasets["application_test"].isnull().sum()/datasets["application_test"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["application_test"].isna().sum().sort_values(ascending = False)
missing_application_test_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Test Missing Count"])
missing_application_test_data.head(20)


# Filter rows with count of missing values greater than 0
missing_application_test_data = missing_application_test_data[missing_application_test_data['Test Missing Count'] > 0]
print("Number of columns with missing value greater than 0:",len(missing_application_test_data))
missing_more_than_60=missing_application_test_data[missing_application_test_data['Percent'] >=60]
print("Number of columns with more than 60% missing values:", len(missing_more_than_60))

plt.figure(figsize=(15, 25))

sns.set_style("whitegrid")
ax = sns.barplot(x="Test Missing Count", y=missing_application_test_data.index, data=missing_application_test_data,
                 palette="rocket")

# Set the figure size and axis labels

ax.set_xlabel("Number of Missing Values ")
ax.set_ylabel("Features")

plt.title('Missing values in Test columns')

# Add values to the bars
for i, v in enumerate(missing_application_test_data['Test Missing Count']):
    ax.text(v + 10, i + .25, str(v), color='black', fontweight='bold')

# Show the plot
plt.show()

Number of columns with missing value greater than 0: 64
Number of columns with more than 60% missing values: 17


datasets["application_train"] = datasets["application_train"].drop(['NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MEDI'], axis=1)
datasets["application_train"].shape

(307511, 119)


datasets["application_test"] = datasets["application_test"].drop(['NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_AVG', 'NONLIVINGAPARTMENTS_MEDI'], axis=1)
datasets["application_test"].shape

(48744, 118)


# Create a bar plot of the distribution of the target column
sns.set(style='darkgrid')
ax = sns.countplot(x='TARGET', data=datasets['application_train'])

# Set the plot title and axis labels
plt.title('Distribution of Target Column')
plt.xlabel('TARGET')
plt.ylabel('Count')

# Set the tick labels for the x-axis
plt.xticks([0, 1], ['Loan Repaid', 'Loan Not-Repaid'])

# Add percentage values on top of the bars
total = float(len(datasets['application_train']))
for p in ax.patches:
    percentage = '{:.1f}%'.format(100 * p.get_height() / total)
    x = p.get_x() + p.get_width() / 2
    y = p.get_height()
    ax.annotate(percentage, (x, y), ha='center', va='center', fontsize=12, color='black', xytext=(0, 5),
                textcoords='offset points')

# Show the plot
plt.show()


correlations = datasets["application_train"].corr()['TARGET'].sort_values()
print('Most Positive Correlations:\n', correlations.tail(10))
print('\nMost Negative Correlations:\n', correlations.head(10))

Most Positive Correlations:
 FLAG_DOCUMENT_3                0.044346
REG_CITY_NOT_LIVE_CITY         0.044395
FLAG_EMP_PHONE                 0.045982
REG_CITY_NOT_WORK_CITY         0.050994
DAYS_ID_PUBLISH                0.051457
DAYS_LAST_PHONE_CHANGE         0.055218
REGION_RATING_CLIENT           0.058899
REGION_RATING_CLIENT_W_CITY    0.060893
DAYS_BIRTH                     0.078239
TARGET                         1.000000
Name: TARGET, dtype: float64

Most Negative Correlations:
 EXT_SOURCE_3                 -0.178919
EXT_SOURCE_2                 -0.160472
EXT_SOURCE_1                 -0.155317
DAYS_EMPLOYED                -0.044932
FLOORSMAX_AVG                -0.044003
FLOORSMAX_MEDI               -0.043768
FLOORSMAX_MODE               -0.043226
AMT_GOODS_PRICE              -0.039645
REGION_POPULATION_RELATIVE   -0.037227
ELEVATORS_AVG                -0.034199
Name: TARGET, dtype: float64


correlations = datasets["application_train"].corr()
sns.set_theme(style="white")
sns.heatmap(correlations)

<Axes: >


# First pie chart - loan data by gender
fig, ax = plt.subplots(figsize=(6, 6))

gender_counts = datasets["application_train"]['CODE_GENDER'].value_counts()
gender_labels = gender_counts.index
gender_sizes = gender_counts.values
colors = ["#497AA7", "#73A4CA"]
ax.pie(gender_sizes, labels=gender_labels, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 12}, colors=colors)
# Add a common title for the figure
ax.set_title('Number of Loans taken based on gender ', fontsize=16)

# Set the aspect ratio to "equal" and the x and y limits to the same value to make it a perfect circle
ax.set_aspect('equal')
ax.set_xlim([-1.1, 1.1])
ax.set_ylim([-1.1, 1.1])

# Show the plot
plt.show()


fig,(ax2,ax3)=plt.subplots(1,2, figsize=(10, 5))
colors = ['#FEB5D4', "#E7C5FF"]
# Second pie chart - loan repaid by gender
gender_loan_repaid = datasets["application_train"][datasets["application_train"]['TARGET'] == 0]
gender_loan_repaid_counts = gender_loan_repaid['CODE_GENDER'].value_counts()
gender_loan_repaid_labels = gender_loan_repaid_counts.index
gender_loan_repaid_sizes = gender_loan_repaid_counts.values
ax2.pie(gender_loan_repaid_sizes, labels=gender_loan_repaid_labels, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 12}, colors=colors)
ax2.set_title('Loan Repaid by Gender')

# Third pie chart - loan not repaid by gender
gender_loan_not_repaid = datasets["application_train"][datasets["application_train"]['TARGET'] == 1]
gender_loan_not_repaid_counts = gender_loan_not_repaid['CODE_GENDER'].value_counts()
gender_loan_not_repaid_labels = gender_loan_not_repaid_counts.index
gender_loan_not_repaid_sizes = gender_loan_not_repaid_counts.values
ax3.pie(gender_loan_not_repaid_sizes, labels=gender_loan_not_repaid_labels, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 12}, colors=colors)
ax3.set_title('Loan Not Repaid by Gender')



# Adjust the layout and spacing of the subplots
plt.tight_layout(pad=2)

# Show the plot
plt.show()


plt.hist(datasets["application_train"]['DAYS_BIRTH'] / -365, edgecolor = 'k', bins = 25, color = "#FFD66A")
plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');


plt.figure(figsize = (12, 6))
sns.kdeplot(datasets["application_train"].loc[datasets["application_train"]['TARGET']==0, 'DAYS_BIRTH'] / -365, color='purple')
sns.kdeplot(datasets["application_train"].loc[datasets["application_train"]['TARGET']==1, 'DAYS_BIRTH'] / -365, color='green')
plt.xlabel('Age of applicant')
plt.title('Repayment of loan based on age', fontsize=16)
plt.show()


fig, axs = plt.subplots(ncols=1, figsize=(12, 6))
# Create a countplot of NAME_HOUSING_TYPE vs TARGET
sns.countplot(x='NAME_HOUSING_TYPE', hue='TARGET', data=datasets['application_train'], palette = 'crest')
axs.set_title('Applicants Housing Situation')
axs.set_ylabel('Count')
axs.set_xlabel('Housing Type')
plt.show()


# Create a pair of subplots with a shared y-axis
fig, axs = plt.subplots(ncols=2, figsize=(14, 6))

# Create a countplot of FLAG_OWN_CAR vs TARGET
sns.countplot(x='FLAG_OWN_CAR', hue='TARGET', data=datasets['application_train'], ax=axs[0],palette='Set1')
axs[0].set_title('Loan Repaid Based on Car Ownership')

# Create a countplot of FLAG_OWN_REALTY vs TARGET
sns.countplot(x='FLAG_OWN_REALTY', hue='TARGET', data=datasets['application_train'], ax=axs[1],palette='Set1')
axs[1].set_title('Loan Repaid Based on Realty Ownership')

# Set the y-axis label
axs[0].set_ylabel('Count')
axs[1].set_ylabel('Count')

# Set the x-axis label
axs[0].set_xlabel('Car Ownership')
axs[1].set_xlabel('Realty Ownership')

# Show the plot
plt.show()


# First pie chart - loan data by type
fig, ax = plt.subplots(figsize=(6, 6))

loantype_counts = datasets["application_train"]['NAME_CONTRACT_TYPE'].value_counts()
loantype_labels = loantype_counts.index
loantype_sizes = loantype_counts.values
colors = ['#A7E8CB', '#C4DDFF']
ax.pie(loantype_sizes, labels=loantype_labels, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 12}, colors=colors)
# Add a common title for the figure
ax.set_title('Number of Loans taken based on loan type', fontsize=16)

# Set the aspect ratio to "equal" and the x and y limits to the same value to make it a perfect circle
ax.set_aspect('equal')
ax.set_xlim([-1.1, 1.1])
ax.set_ylim([-1.1, 1.1])

# Show the plot
plt.show()


fig, axs = plt.subplots(ncols=1, figsize=(12, 6))

# Create a countplot of NAME_EDUCATION_TYPE vs TARGET
sns.countplot(x='NAME_EDUCATION_TYPE', hue='TARGET', data=datasets['application_train'], palette = 'rocket')
axs.set_title('Education level of the Applicant')

# Set the y-axis label
axs.set_ylabel('Count')
axs.set_xlabel('Education Level')

# Show the plot
plt.show()


fig, axs = plt.subplots(ncols=1, figsize=(20, 24))

# Create a countplot of ORGANIZATION_TYPE vs TARGET
sns.countplot(y='ORGANIZATION_TYPE', hue='TARGET', data=datasets['application_train'], palette = 'rocket')
axs.set_title('Organisation that the Applicant is working in')

# Set the x-axis label
axs.set_xlabel('Count')
axs.set_ylabel('Organisation')

# Show the plot
plt.show()


sns.countplot(x='OCCUPATION_TYPE', data=datasets["application_train"]);
plt.title('Applicants Occupation');
plt.xticks(rotation=90);


plt.figure(figsize=(12, 6)) 
sns.boxplot(x=datasets['application_train']['AMT_CREDIT'], palette='Set2') 
plt.xlabel('Credit')
plt.title('Boxplot for Credit') 
plt.show()


plt.figure(figsize=(12, 6)) 
sns.distplot(x=datasets['application_train']['AMT_ANNUITY'], color='purple') 
plt.xlabel('Annuity')
plt.title('Distribution plot for Annuity') 
plt.show()


plt.figure(figsize=(12, 6)) 
sns.boxplot(x=datasets['application_train']['AMT_GOODS_PRICE'], palette='Set2') 
plt.xlabel('Goods Price')
plt.title('Boxplot for Goods Price') 
plt.show()


datasets["bureau"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1716428 entries, 0 to 1716427
Data columns (total 17 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_CURR              int64  
 1   SK_ID_BUREAU            int64  
 2   CREDIT_ACTIVE           object 
 3   CREDIT_CURRENCY         object 
 4   DAYS_CREDIT             int64  
 5   CREDIT_DAY_OVERDUE      int64  
 6   DAYS_CREDIT_ENDDATE     float64
 7   DAYS_ENDDATE_FACT       float64
 8   AMT_CREDIT_MAX_OVERDUE  float64
 9   CNT_CREDIT_PROLONG      int64  
 10  AMT_CREDIT_SUM          float64
 11  AMT_CREDIT_SUM_DEBT     float64
 12  AMT_CREDIT_SUM_LIMIT    float64
 13  AMT_CREDIT_SUM_OVERDUE  float64
 14  CREDIT_TYPE             object 
 15  DAYS_CREDIT_UPDATE      int64  
 16  AMT_ANNUITY             float64
dtypes: float64(8), int64(6), object(3)
memory usage: 222.6+ MB


datasets["bureau"].describe(include='all')


percent = (datasets["bureau"].isnull().sum()/datasets["bureau"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau"].isna().sum().sort_values(ascending = False)
total_count_with = datasets["bureau"].isnull().count()
total_count_without = datasets["bureau"].count()
missing_application_train_data  = pd.concat([percent, sum_missing, total_count_without, total_count_with], axis=1, keys=['Percent', 'Missing Count', 'Total Count without Missing value', 'Total Count'])
missing_application_train_data


percent = (datasets['bureau'].isnull().sum()/datasets['bureau'].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets['bureau'].isna().sum().sort_values(ascending = False)
missing_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Missing Count"])
missing_data=missing_data[missing_data['Percent'] > 0]
f, ax = plt.subplots()
plt.title(f'Missing data for bureau (%)', fontsize=11)
fig=sns.barplot(x = missing_data["Percent"],y = missing_data.index ,alpha=0.8)
plt.xlabel('Missing values (%)', fontsize=10)
plt.ylabel('Features', fontsize=10)

Text(0, 0.5, 'Features')


bureau_df = datasets['bureau']
application_train_df = datasets['application_train']
merged_df = application_train_df[['SK_ID_CURR', 'TARGET']].merge(bureau_df, on='SK_ID_CURR', how='left')

merged_df.head(5)


fig = plt.figure(figsize=(15,10))
sns.heatmap(merged_df.corr(), annot=True)

<Axes: >


# Removing the outlier which was affecting our plot
merged_df = merged_df[merged_df["SK_ID_CURR"] != 104463]  
plt.figure(figsize=(10, 8))
sns.scatterplot(x = "DAYS_CREDIT", y='DAYS_ENDDATE_FACT', hue='TARGET' ,data=merged_df)
plt.title('Days Since Credit Inquiry vs. Days Since Credit Ended W.R.T Loan Repayment Status')
plt.xlabel('Days Since Credit Inquiry')
plt.ylabel('Days Since Credit Ended')
plt.show()


datasets["bureau_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 27299925 entries, 0 to 27299924
Data columns (total 3 columns):
 #   Column          Dtype 
---  ------          ----- 
 0   SK_ID_BUREAU    int64 
 1   MONTHS_BALANCE  int64 
 2   STATUS          object
dtypes: int64(2), object(1)
memory usage: 624.8+ MB


datasets["bureau_balance"].describe(include='all')


percent = (datasets["bureau_balance"].isnull().sum()/datasets["bureau_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["bureau_balance"].isna().sum().sort_values(ascending = False)
total_count_with = datasets["bureau_balance"].isnull().count()
total_count_without = datasets["bureau_balance"].count()
missing_application_train_data  = pd.concat([percent, sum_missing, total_count_without, total_count_with], axis=1, keys=['Percent', 'Missing Count', 'Total Count without Missing value', 'Total Count'])
missing_application_train_data


bureau_balance_df = datasets['bureau_balance']
bureau_balance_merged_df = merged_df[['SK_ID_BUREAU', 'TARGET']].merge(bureau_balance_df, on='SK_ID_BUREAU', how='left')

bureau_balance_merged_df.head(5)


fig = plt.figure(figsize=(5,3))
sns.heatmap(bureau_balance_merged_df.corr(), annot=True)

<Axes: >


datasets["credit_card_balance"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3840312 entries, 0 to 3840311
Data columns (total 23 columns):
 #   Column                      Dtype  
---  ------                      -----  
 0   SK_ID_PREV                  int64  
 1   SK_ID_CURR                  int64  
 2   MONTHS_BALANCE              int64  
 3   AMT_BALANCE                 float64
 4   AMT_CREDIT_LIMIT_ACTUAL     int64  
 5   AMT_DRAWINGS_ATM_CURRENT    float64
 6   AMT_DRAWINGS_CURRENT        float64
 7   AMT_DRAWINGS_OTHER_CURRENT  float64
 8   AMT_DRAWINGS_POS_CURRENT    float64
 9   AMT_INST_MIN_REGULARITY     float64
 10  AMT_PAYMENT_CURRENT         float64
 11  AMT_PAYMENT_TOTAL_CURRENT   float64
 12  AMT_RECEIVABLE_PRINCIPAL    float64
 13  AMT_RECIVABLE               float64
 14  AMT_TOTAL_RECEIVABLE        float64
 15  CNT_DRAWINGS_ATM_CURRENT    float64
 16  CNT_DRAWINGS_CURRENT        int64  
 17  CNT_DRAWINGS_OTHER_CURRENT  float64
 18  CNT_DRAWINGS_POS_CURRENT    float64
 19  CNT_INSTALMENT_MATURE_CUM   float64
 20  NAME_CONTRACT_STATUS        object 
 21  SK_DPD                      int64  
 22  SK_DPD_DEF                  int64  
dtypes: float64(15), int64(7), object(1)
memory usage: 673.9+ MB


datasets["credit_card_balance"].describe(include='all')


percent = (datasets["credit_card_balance"].isnull().sum()/datasets["credit_card_balance"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["credit_card_balance"].isna().sum().sort_values(ascending = False)
total_count_with = datasets["credit_card_balance"].isnull().count()
total_count_without = datasets["credit_card_balance"].count()
missing_application_train_data  = pd.concat([percent, sum_missing, total_count_without, total_count_with], axis=1, keys=['Percent', 'Missing Count', 'Total Count without Missing value', 'Total Count'])
missing_application_train_data


percent = (datasets['credit_card_balance'].isnull().sum()/datasets['credit_card_balance'].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets['credit_card_balance'].isna().sum().sort_values(ascending = False)
missing_data = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Missing Count"])
missing_data = missing_data[missing_data['Percent'] > 0]
plt.title(f'Missing data for credit_card_balance in percent', fontsize=10)
sns.barplot(x = missing_data["Percent"],y = missing_data.index ,alpha=0.8)
plt.xlabel('Missing values (%)', fontsize=10)
plt.ylabel('Features', fontsize=10)
plt.show()


fig, ax = plt.subplots()
ax.hist(datasets['credit_card_balance']['AMT_PAYMENT_TOTAL_CURRENT'], bins=50)
ax.set_title('Distribution of AMT_PAYMENT_TOTAL_CURRENT')
plt.xlabel("Total Amount in the corresponding month")
plt.ylabel("Count")
ax.tick_params(labelsize=10,length=0)
plt.box(False)
plt.ticklabel_format(style='plain', axis='y')
ax.ticklabel_format(useOffset=False, style='plain')
ax.yaxis.grid(linewidth=0.5,color='black')
ax.set_axisbelow(True)


fig, ax = plt.subplots()
sns.histplot(data=datasets['credit_card_balance'], x='AMT_CREDIT_LIMIT_ACTUAL', kde=True)
plt.title('Distribution of Credit Card Limits')
plt.xlabel('Credit Card Limit')
plt.ylabel('Count')
ax.ticklabel_format(useOffset=False, style='plain')
plt.show()


fig, ax = plt.subplots()
datasets['credit_card_balance']["credit_limit"] = datasets['credit_card_balance']["AMT_CREDIT_LIMIT_ACTUAL"] >= datasets['credit_card_balance']["AMT_BALANCE"]
datasets['credit_card_balance']["credit_limit"] = datasets['credit_card_balance']["credit_limit"].apply(lambda x: "Less spent" if x == True else "More spent")
sns.scatterplot(data=datasets['credit_card_balance'], x='AMT_CREDIT_LIMIT_ACTUAL', y='AMT_DRAWINGS_CURRENT', hue = "credit_limit")
plt.title('Credit Card Limit vs. Amount Spent During the Month')
plt.xlabel('Credit Card Limit')
plt.ylabel('Amount Spent During the Month')
ax.ticklabel_format(useOffset=False, style='plain')
plt.show()


merged_df = datasets['application_train'][['SK_ID_CURR', 'TARGET']].merge(datasets['credit_card_balance'], on='SK_ID_CURR', how='left')
merged_df.head(5)


merged_df.corr()['TARGET'].sort_values(ascending= False)

TARGET                        1.000000
AMT_BALANCE                   0.050098
AMT_TOTAL_RECEIVABLE          0.049839
AMT_RECIVABLE                 0.049803
AMT_RECEIVABLE_PRINCIPAL      0.049692
AMT_INST_MIN_REGULARITY       0.039798
CNT_DRAWINGS_ATM_CURRENT      0.038437
CNT_DRAWINGS_CURRENT          0.037793
MONTHS_BALANCE                0.035695
CNT_DRAWINGS_POS_CURRENT      0.029536
AMT_DRAWINGS_ATM_CURRENT      0.024700
AMT_DRAWINGS_CURRENT          0.022378
AMT_CREDIT_LIMIT_ACTUAL       0.013823
AMT_PAYMENT_CURRENT           0.012929
AMT_PAYMENT_TOTAL_CURRENT     0.012302
SK_DPD_DEF                    0.010538
AMT_DRAWINGS_POS_CURRENT      0.005084
AMT_DRAWINGS_OTHER_CURRENT    0.003843
CNT_DRAWINGS_OTHER_CURRENT    0.003044
SK_ID_PREV                    0.002571
SK_DPD                        0.001684
SK_ID_CURR                   -0.004412
CNT_INSTALMENT_MATURE_CUM    -0.023684
Name: TARGET, dtype: float64


fig = plt.figure(figsize=(20,15))
sns.heatmap(merged_df.corr(), annot=True)

<Axes: >


edaPAdf = pd.DataFrame()


edaPAdf['SK_ID_CURR'] = datasets['application_train']['SK_ID_CURR']
edaPAdf['TARGET'] = datasets['application_train']['TARGET']


datasets['previous_application'].shape

(1670214, 37)


datasets['previous_application'].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1670214 entries, 0 to 1670213
Data columns (total 37 columns):
 #   Column                       Non-Null Count    Dtype  
---  ------                       --------------    -----  
 0   SK_ID_PREV                   1670214 non-null  int64  
 1   SK_ID_CURR                   1670214 non-null  int64  
 2   NAME_CONTRACT_TYPE           1670214 non-null  object 
 3   AMT_ANNUITY                  1297979 non-null  float64
 4   AMT_APPLICATION              1670214 non-null  float64
 5   AMT_CREDIT                   1670213 non-null  float64
 6   AMT_DOWN_PAYMENT             774370 non-null   float64
 7   AMT_GOODS_PRICE              1284699 non-null  float64
 8   WEEKDAY_APPR_PROCESS_START   1670214 non-null  object 
 9   HOUR_APPR_PROCESS_START      1670214 non-null  int64  
 10  FLAG_LAST_APPL_PER_CONTRACT  1670214 non-null  object 
 11  NFLAG_LAST_APPL_IN_DAY       1670214 non-null  int64  
 12  RATE_DOWN_PAYMENT            774370 non-null   float64
 13  RATE_INTEREST_PRIMARY        5951 non-null     float64
 14  RATE_INTEREST_PRIVILEGED     5951 non-null     float64
 15  NAME_CASH_LOAN_PURPOSE       1670214 non-null  object 
 16  NAME_CONTRACT_STATUS         1670214 non-null  object 
 17  DAYS_DECISION                1670214 non-null  int64  
 18  NAME_PAYMENT_TYPE            1670214 non-null  object 
 19  CODE_REJECT_REASON           1670214 non-null  object 
 20  NAME_TYPE_SUITE              849809 non-null   object 
 21  NAME_CLIENT_TYPE             1670214 non-null  object 
 22  NAME_GOODS_CATEGORY          1670214 non-null  object 
 23  NAME_PORTFOLIO               1670214 non-null  object 
 24  NAME_PRODUCT_TYPE            1670214 non-null  object 
 25  CHANNEL_TYPE                 1670214 non-null  object 
 26  SELLERPLACE_AREA             1670214 non-null  int64  
 27  NAME_SELLER_INDUSTRY         1670214 non-null  object 
 28  CNT_PAYMENT                  1297984 non-null  float64
 29  NAME_YIELD_GROUP             1670214 non-null  object 
 30  PRODUCT_COMBINATION          1669868 non-null  object 
 31  DAYS_FIRST_DRAWING           997149 non-null   float64
 32  DAYS_FIRST_DUE               997149 non-null   float64
 33  DAYS_LAST_DUE_1ST_VERSION    997149 non-null   float64
 34  DAYS_LAST_DUE                997149 non-null   float64
 35  DAYS_TERMINATION             997149 non-null   float64
 36  NFLAG_INSURED_ON_APPROVAL    997149 non-null   float64
dtypes: float64(15), int64(6), object(16)
memory usage: 471.5+ MB


datasets['previous_application'].describe()


datasets['previous_application'].describe(include='all')


edaPAdf = edaPAdf.merge(datasets["previous_application"], how='left', on='SK_ID_CURR')


percent = (edaPAdf.isnull().sum()/edaPAdf.isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = edaPAdf.isna().sum().sort_values(ascending = False)
missing_previous_application_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Missing Count"])
missing_previous_application_data.head(20)


# Filter rows with count of missing values greater than 0
missing_previous_application_data = missing_previous_application_data[missing_previous_application_data['Missing Count'] > 0]
print("Number of columns with missing value greater than 0:",len(missing_previous_application_data))
missing_more_than_60=missing_previous_application_data[missing_previous_application_data['Percent'] >=60]
print("Number of columns with more than 60% missing values:", len(missing_more_than_60))

plt.figure(figsize=(15, 20))

sns.set_style("whitegrid")
ax = sns.barplot(x="Missing Count", y=missing_previous_application_data.index, data=missing_previous_application_data,
                 palette="rocket")

# Set the figure size and axis labels

ax.set_xlabel("Number of Missing Values ")
ax.set_ylabel("Features")

plt.title('Missing values in previous application columns')

# Add values to the bars
for i, v in enumerate(missing_previous_application_data['Missing Count']):
    ax.text(v + 10, i + .25, str(v), color='black', fontweight='bold')

# Show the plot
plt.show()

Number of columns with missing value greater than 0: 36
Number of columns with more than 60% missing values: 2


edaPAdf.corr()['TARGET']

SK_ID_CURR                  -0.001216
TARGET                       1.000000
SK_ID_PREV                   0.002009
AMT_ANNUITY                 -0.014922
AMT_APPLICATION             -0.005583
AMT_CREDIT                  -0.002350
AMT_DOWN_PAYMENT            -0.016918
AMT_GOODS_PRICE              0.000254
HOUR_APPR_PROCESS_START     -0.027809
NFLAG_LAST_APPL_IN_DAY      -0.002887
RATE_DOWN_PAYMENT           -0.026111
RATE_INTEREST_PRIMARY       -0.001470
RATE_INTEREST_PRIVILEGED     0.028640
DAYS_DECISION                0.039901
SELLERPLACE_AREA            -0.002539
CNT_PAYMENT                  0.030480
DAYS_FIRST_DRAWING          -0.031154
DAYS_FIRST_DUE              -0.006651
DAYS_LAST_DUE_1ST_VERSION    0.018021
DAYS_LAST_DUE                0.017522
DAYS_TERMINATION             0.016981
NFLAG_INSURED_ON_APPROVAL    0.000653
Name: TARGET, dtype: float64


correlations = edaPAdf.corr()
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(correlations, cmap = cmap)

<Axes: >


fig, ax = plt.subplots(figsize=(6, 6))

contract_type_counts = edaPAdf['NAME_CONTRACT_TYPE'].value_counts()
contract_type_labels = contract_type_counts.index
contract_type_sizes = contract_type_counts.values
colors = ["#73A4CA", "#497AA7", "#2E5B88"]
ax.pie(contract_type_sizes, labels=contract_type_labels, autopct='%1.1f%%', startangle=90, textprops={'fontsize': 12}, colors=colors)
# Add a common title for the figure
ax.set_title('Contract type of the previous applications', fontsize=16)

# Set the aspect ratio to "equal" and the x and y limits to the same value to make it a perfect circle
ax.set_aspect('equal')
ax.set_xlim([-1.1, 1.1])
ax.set_ylim([-1.1, 1.1])

# Show the plot
plt.show()


sns.countplot(x='NAME_CONTRACT_STATUS', data=edaPAdf, palette = "Set3");
plt.title('Contract status of the previous applicant');
plt.xlabel('Contract Status')
plt.xticks(rotation=90);


fig, axs = plt.subplots(ncols=1, figsize=(12, 6))
sns.countplot(x='NAME_CLIENT_TYPE', hue='TARGET', data=edaPAdf, palette = 'rocket')
axs.set_title('Client old or new vs Target')
plt.xlabel('Client Type')
axs.set_ylabel('Count')
plt.show()


plt.figure(figsize=(12, 6)) 
sns.distplot(x=edaPAdf['AMT_CREDIT'], color='maroon') 
plt.xlabel('Credit')
plt.title('Distribution plot for Credit') 
plt.show()


edaPCBdf = pd.DataFrame()


edaPCBdf['SK_ID_CURR'] = datasets['application_train']['SK_ID_CURR']
edaPCBdf['TARGET'] = datasets['application_train']['TARGET']


datasets['POS_CASH_balance'].shape

(10001358, 8)


datasets['POS_CASH_balance'].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10001358 entries, 0 to 10001357
Data columns (total 8 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   SK_ID_PREV             int64  
 1   SK_ID_CURR             int64  
 2   MONTHS_BALANCE         int64  
 3   CNT_INSTALMENT         float64
 4   CNT_INSTALMENT_FUTURE  float64
 5   NAME_CONTRACT_STATUS   object 
 6   SK_DPD                 int64  
 7   SK_DPD_DEF             int64  
dtypes: float64(2), int64(5), object(1)
memory usage: 610.4+ MB


datasets['POS_CASH_balance'].describe()


datasets['POS_CASH_balance'].describe(include='all')


edaPCBdf = edaPCBdf.merge(datasets["POS_CASH_balance"], how='left', on='SK_ID_CURR')


percent = (edaPCBdf.isnull().sum()/edaPCBdf.isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = edaPCBdf.isna().sum().sort_values(ascending = False)
missing_POS_CASH_balance_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Missing Count"])
missing_POS_CASH_balance_data.head(20)


# Filter rows with count of missing values greater than 0
missing_POS_CASH_balance_data = missing_POS_CASH_balance_data[missing_POS_CASH_balance_data['Missing Count'] > 0]
print("Number of columns with missing value greater than 0:",len(missing_POS_CASH_balance_data))
missing_more_than_60=missing_POS_CASH_balance_data[missing_POS_CASH_balance_data['Percent'] >=60]
print("Number of columns with more than 60% missing values:", len(missing_more_than_60))

plt.figure(figsize=(10, 6))

sns.set_style("whitegrid")
ax = sns.barplot(x="Missing Count", y=missing_POS_CASH_balance_data.index, data=missing_POS_CASH_balance_data,
                 palette="rocket")

# Set the figure size and axis labels

ax.set_xlabel("Number of Missing Values ")
ax.set_ylabel("Features")

plt.title('Missing values in POS CASH Balance columns')

# Add values to the bars
for i, v in enumerate(missing_POS_CASH_balance_data['Missing Count']):
    ax.text(v + 10, i + .25, str(v), color='black', fontweight='bold')

# Show the plot
plt.show()

Number of columns with missing value greater than 0: 7
Number of columns with more than 60% missing values: 0


edaPCBdf.corr()['TARGET']

SK_ID_CURR              -0.002244
TARGET                   1.000000
SK_ID_PREV              -0.000056
MONTHS_BALANCE           0.020147
CNT_INSTALMENT           0.018506
CNT_INSTALMENT_FUTURE    0.021972
SK_DPD                   0.009866
SK_DPD_DEF               0.008594
Name: TARGET, dtype: float64


correlations = edaPCBdf.corr()
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(correlations, cmap = cmap)

<Axes: >


fig, axs = plt.subplots(ncols=1, figsize=(10, 25))
sns.countplot(y='CNT_INSTALMENT_FUTURE', hue='TARGET', data=edaPCBdf, palette = 'Set1')
axs.set_title('Installments left to pay vs Target')
axs.set_xlabel('Count')
axs.set_ylabel('Installments left to pay')
plt.show()


fig, axs = plt.subplots(ncols=1, figsize=(15, 7))
sns.countplot(x='NAME_CONTRACT_STATUS', data=edaPCBdf, palette = 'crest')
axs.set_title('Contract status during the month')
axs.set_ylabel('Count')
plt.show()


datasets["installments_payments"].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13605401 entries, 0 to 13605400
Data columns (total 8 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   SK_ID_PREV              int64  
 1   SK_ID_CURR              int64  
 2   NUM_INSTALMENT_VERSION  float64
 3   NUM_INSTALMENT_NUMBER   int64  
 4   DAYS_INSTALMENT         float64
 5   DAYS_ENTRY_PAYMENT      float64
 6   AMT_INSTALMENT          float64
 7   AMT_PAYMENT             float64
dtypes: float64(5), int64(3)
memory usage: 830.4 MB


datasets["installments_payments"].describe(include='all')


percent = (datasets["installments_payments"].isnull().sum()/datasets["installments_payments"].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets["installments_payments"].isna().sum().sort_values(ascending = False)
total_count_with = datasets["installments_payments"].isnull().count()
total_count_without = datasets["installments_payments"].count()
missing_application_train_data  = pd.concat([percent, sum_missing, total_count_without, total_count_with], axis=1, keys=['Percent', 'Missing Count', 'Total Count without Missing value', 'Total Count'])
missing_application_train_data


percent = (datasets['installments_payments'].isnull().sum()/datasets['installments_payments'].isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = datasets['installments_payments'].isna().sum().sort_values(ascending = False)
missing_data  = pd.concat([percent, sum_missing], axis=1, keys=['Percent', "Missing Count"])
missing_data=missing_data[missing_data['Percent'] > 0]
fig, ax = plt.subplots()
plt.title(f'Missing data for installments_payments in percent', fontsize=10)
sns.barplot(x = missing_data["Percent"],y = missing_data.index ,alpha=0.8)
plt.xlabel('Missing values (%)', fontsize=10)
plt.ylabel('Features', fontsize=10)

Text(0, 0.5, 'Features')


inst_pay = pd.DataFrame()
inst_pay['AMT_DIFF'] = datasets["installments_payments"]['AMT_INSTALMENT'] - datasets["installments_payments"]['AMT_PAYMENT']
inst_pay['OVER_PAYMENT'] = inst_pay['AMT_DIFF'].apply(lambda x: 1 if x < 0 else 0)
inst_pay['PARTIAL_PAYMENT'] = inst_pay['AMT_DIFF'].apply(lambda x: 1 if x > 0 else 0)
inst_pay['FULL_PAYMENT'] = inst_pay['AMT_DIFF'].apply(lambda x: 1 if x == 0 else 0)

full_payment_pct = inst_pay['FULL_PAYMENT'].sum() / inst_pay.shape[0] * 100
partial_payment_pct = inst_pay['PARTIAL_PAYMENT'].sum() / inst_pay.shape[0] * 100
over_payment_pct = inst_pay['OVER_PAYMENT'].sum() / inst_pay.shape[0] * 100

labels = ['Full Payment', 'Partial Payment', 'Over Payment']
x = [full_payment_pct, partial_payment_pct, over_payment_pct]
colors = ['#FF671F','#046A38','#06038D']
explode = (0, 0.05, -0.02)
wedge_properties = {"edgecolor":"k",'width': .25}

plt.pie(x, labels=labels, explode=explode, colors=colors,
        startangle=150, counterclock=False, shadow=True,
        wedgeprops=wedge_properties, autopct="%1.1f%%")
plt.title("Distribution of Installment Payment Scenarios")
plt.show()


payment_counts = [inst_pay['FULL_PAYMENT'].sum(), inst_pay['PARTIAL_PAYMENT'].sum(), inst_pay['OVER_PAYMENT'].sum()]
payment_labels = ['Full Payment', 'Partial Payment', 'Over Payment']
columns = {
 'Payment Scenarios': payment_labels,
 'Count': payment_counts
}
df = pd.DataFrame(columns)

fig, ax = plt.subplots()
sns.barplot(x = 'Payment Scenarios', y = 'Count', data = df, ci=95, ax=ax, palette = 'plasma')
ax.set_title('Installment Payment Scenarios')
ax.tick_params(labelsize=10,length=0)
plt.box(False)
plt.ticklabel_format(style='plain', axis='y')
ax.yaxis.grid(linewidth=0.5,color='black')
ax.set_axisbelow(True)

for index, row in df.iterrows():
    ax.text(row.name, row.Count, round(row.Count), color='black', ha='center')


installment_versions = datasets["installments_payments"]["NUM_INSTALMENT_VERSION"].value_counts()
installment_versions = installment_versions[installment_versions > 1000]
columns = {
 'Installment Versions': installment_versions.index.astype('int64'),
 'Count': installment_versions.values
}
df = pd.DataFrame(columns)
fig, ax = plt.subplots()
sns.barplot(x = 'Installment Versions', y = 'Count', data = df)
ax.set_title('Count of Different Installment Versions')
plt.xticks(rotation=90)
ax.tick_params(labelsize=10,length=0)
plt.box(False)
plt.ticklabel_format(style='plain', axis='y')
ax.yaxis.grid(linewidth=0.5,color='black')
ax.set_axisbelow(True)


total_payments = datasets["installments_payments"]["AMT_PAYMENT"].sum()
payments_by_installment = datasets["installments_payments"].groupby("NUM_INSTALMENT_NUMBER")["AMT_PAYMENT"].sum()
percentages = payments_by_installment / total_payments * 100
percentages = percentages[percentages > 2.0]
plt.pie(percentages.values, labels=percentages.index, autopct='%1.1f%%')
plt.title("Percentage of Total Payments Made by Installment Number")
plt.show()


merged_df = datasets['application_train'][['SK_ID_CURR', 'TARGET']].merge(datasets['installments_payments'], on='SK_ID_CURR', how='left')
merged_df.head(5)


merged_df.corr()['TARGET'].abs().sort_values(ascending= False)

TARGET                    1.000000
DAYS_ENTRY_PAYMENT        0.035122
DAYS_INSTALMENT           0.034974
NUM_INSTALMENT_NUMBER     0.016190
NUM_INSTALMENT_VERSION    0.009896
AMT_PAYMENT               0.003623
SK_ID_CURR                0.002533
AMT_INSTALMENT            0.001498
SK_ID_PREV                0.000212
Name: TARGET, dtype: float64


fig = plt.figure(figsize=(12,10))
sns.heatmap(merged_df.corr(), annot=True)

<Axes: >


list(datasets.keys())

['application_train',
 'application_test',
 'bureau',
 'bureau_balance',
 'credit_card_balance',
 'installments_payments',
 'previous_application',
 'POS_CASH_balance']


len(datasets["application_train"]["SK_ID_CURR"].unique()) == datasets["application_train"].shape[0]

True


# is there an overlap between the test and train customers 
np.intersect1d(datasets["application_train"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])

array([], dtype=int64)


# 
datasets["application_test"].shape

(48744, 118)


datasets["application_train"].shape

(307511, 119)


appsDF = datasets["previous_application"]
display(appsDF.head())
print(f"{appsDF.shape[0]:,} rows, {appsDF.shape[1]:,} columns")

1,670,214 rows, 37 columns


print(f"There are  {appsDF.shape[0]:,} previous applications")

There are  1,670,214 previous applications


#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_train"]["SK_ID_CURR"])):,}')

Number of train applicants with previous applications is 291,057


#Find the intersection of two arrays.
print(f'Number of train applicants with previous applications is {len(np.intersect1d(datasets["previous_application"]["SK_ID_CURR"], datasets["application_test"]["SK_ID_CURR"])):,}')

Number of train applicants with previous applications is 47,800


# How many previous applciations  per applicant in the previous_application 
prevAppCounts = appsDF['SK_ID_CURR'].value_counts(dropna=False)
len(prevAppCounts[prevAppCounts >40])  #more that 40 previous applications
plt.hist(prevAppCounts[prevAppCounts>=0], bins=100)
plt.grid()


prevAppCounts[prevAppCounts >50].plot(kind='bar')
plt.xticks(rotation=90)
plt.show()


sum(appsDF['SK_ID_CURR'].value_counts()==1)

60458


plt.hist(appsDF['SK_ID_CURR'].value_counts(), cumulative =True, bins = 100);
plt.grid()
plt.ylabel('cumulative number of IDs')
plt.xlabel('Number of previous applications per ID')
plt.title('Histogram of Number of previous applications for an ID')

Text(0.5, 1.0, 'Histogram of Number of previous applications for an ID')


apps_all = appsDF['SK_ID_CURR'].nunique()
apps_5plus = appsDF['SK_ID_CURR'].value_counts()>=5
apps_40plus = appsDF['SK_ID_CURR'].value_counts()>=40
print('Percentage with 10 or more previous apps:', np.round(100.*(sum(apps_5plus)/apps_all),5))
print('Percentage with 40 or more previous apps:', np.round(100.*(sum(apps_40plus)/apps_all),5))

Percentage with 10 or more previous apps: 41.76895
Percentage with 40 or more previous apps: 0.03453


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704)]["AMT_CREDIT"]

6    0.0
Name: AMT_CREDIT, dtype: float64


appsDF[0:50][(appsDF["SK_ID_CURR"]==175704) & ~(appsDF["AMT_CREDIT"]==1.0)]


#appsDF['agg_op_features'].head()

import pandas as pd
import dateutil

# Load data from csv file
data = pd.DataFrame.from_csv('phone_data.csv')
# Convert date from string to date times
data['date'] = data['date'].apply(dateutil.parser.parse, dayfirst=True)

data.groupby('month', as_index=False).agg({"duration": "sum"})


features = ['AMT_ANNUITY', 'AMT_APPLICATION']
print(f"{appsDF[features].describe()}")
agg_ops = ["min", "max", "mean"]
result = appsDF.groupby(["SK_ID_CURR"], as_index=False).agg("mean") #group by ID
display(result.head())
print("-"*50)
result = appsDF.groupby(["SK_ID_CURR"], as_index=False).agg({'AMT_ANNUITY' : agg_ops, 'AMT_APPLICATION' : agg_ops})
result.columns = result.columns.map('_'.join)
display(result)
result['range_AMT_APPLICATION'] = result['AMT_APPLICATION_max'] - result['AMT_APPLICATION_min']
print(f"result.shape: {result.shape}")
result[0:10]

        AMT_ANNUITY  AMT_APPLICATION
count  1.297979e+06     1.670214e+06
mean   1.595512e+04     1.752339e+05
std    1.478214e+04     2.927798e+05
min    0.000000e+00     0.000000e+00
25%    6.321780e+03     1.872000e+04
50%    1.125000e+04     7.104600e+04
75%    2.065842e+04     1.803600e+05
max    4.180581e+05     6.905160e+06

--------------------------------------------------

result.shape: (338857, 8)


# Create aggregate features (via pipeline)
class FeaturesAggregater(BaseEstimator, TransformerMixin):
    def __init__(self, features=None): # no *args or **kargs
        self.features = features
        self.agg_op_features = {}
        for f in features:
            #self.agg_op_features[f] = {f"{f}_{func}":func for func in ["min", "max", "mean"]}
            self.agg_op_features[f]=["min", "max", "mean","median"]

    def fit(self, X, y=None):
        return self
    
    def transform(self, X, y=None):
        #from IPython.core.debugger import Pdb as pdb;    pdb().set_trace() #breakpoint; dont forget to quit         
        result = X.groupby(["SK_ID_CURR"]).agg(self.agg_op_features)
        #result.columns = result.columns.droplevel()
        result.columns = ["_".join(x) for x in result.columns.ravel()]
        result = result.reset_index(level=["SK_ID_CURR"])
        return result # return dataframe with the join key "SK_ID_CURR"


_#LOAD THE DATASET
X_train= datasets["application_train"]#primary dataset
appsDF = datasets["previous_application"] #prev app
burDF=datasets['bureau']
bureau_balDF=datasets['bureau_balance']
ccbDF=datasets['credit_card_balance']
ipDF=datasets['installments_payments']
posDF=datasets['POS_CASH_balance']

#SELECTED FEATURES ARE TAKEN FROM SECONDARY TABLES BASED ON CORRELATION ANALAYSIS
pr_features = ['AMT_CREDIT','CNT_PAYMENT']
br_features = ['DAYS_CREDIT', 'DAYS_CREDIT_UPDATE','DAYS_ENDDATE_FACT']
cc_features = ['MONTHS_BALANCE', 'AMT_BALANCE', 'CNT_DRAWINGS_ATM_CURRENT']
ip_features = ['DAYS_ENTRY_PAYMENT','DAYS_INSTALMENT', 'AMT_PAYMENT']
pos_features = ['MONTHS_BALANCE','CNT_INSTALMENT_FUTURE', 'CNT_INSTALMENT']


#PREVIOUS APPLICATION TRANSFORMATION


prevApps_feature_pipeline = Pipeline([
        ('prevApps_aggregater', FeaturesAggregater(pr_features)), # Aggregate across old and new features
    ])
prevApps_aggregated = prevApps_feature_pipeline.fit_transform(appsDF)


#Bureau Transformation


bureau_feature_pipeline = Pipeline([
        ('bureau_aggregater', FeaturesAggregater(br_features)), # Aggregate across old and new features
    ])
bureau_aggregated = bureau_feature_pipeline.fit_transform(burDF)

#Credit Card Balance Transformation


ccbal_feature_pipeline = Pipeline([
        ('ccbal_aggregater', FeaturesAggregater(cc_features)), # Aggregate across old and new features
    ])
ccbal_aggregated = ccbal_feature_pipeline.fit_transform(ccbDF)

#Installments Payments Transformation


inspay_feature_pipeline = Pipeline([
        ('inspay_aggregater', FeaturesAggregater(ip_features)), # Aggregate across old and new features
    ])
inspay_aggregated = inspay_feature_pipeline.fit_transform(ipDF)

#POS CASH Balance Transformation


poscashbal_feature_pipeline = Pipeline([
        ('poscashbal_aggregater', FeaturesAggregater(pos_features)), # Aggregate across old and new features
    ])
poscashbal_aggregated = poscashbal_feature_pipeline.fit_transform(posDF)


# merge primary table and secondary tables using features based on meta data and  aggregage stats 
merged_data=True

if merged_data:
    # 1. Join/Merge in prevApps Data
    X_train = X_train.merge(prevApps_aggregated, how='left', on='SK_ID_CURR')

    # 2. Join/Merge in Bureau Data
    X_train = X_train.merge(bureau_aggregated, how='left', on="SK_ID_CURR")

    # 3. Join/Merge in credit_card_balance  Data
    X_train = X_train.merge(ccbal_aggregated, how='left', on="SK_ID_CURR")

    # 4. Join/Merge in Aggregated installments_payments Data
    X_train = X_train.merge(inspay_aggregated, how='left', on="SK_ID_CURR")

    # 5. Join/Merge in Aggregated POS_CASH_balance Data
    X_train = X_train.merge(poscashbal_aggregated, how='left', on="SK_ID_CURR")


if merged_data:
  X_kaggle_test= datasets["application_test"]
  X_kaggle_test = X_kaggle_test.merge(prevApps_aggregated, how='left', on='SK_ID_CURR')
  X_kaggle_test = X_kaggle_test.merge(bureau_aggregated, how='left', on='SK_ID_CURR')
  X_kaggle_test = X_kaggle_test.merge(ccbal_aggregated, how='left', on='SK_ID_CURR')
  X_kaggle_test = X_kaggle_test.merge(inspay_aggregated, how='left', on='SK_ID_CURR')
  X_kaggle_test = X_kaggle_test.merge(poscashbal_aggregated, how='left', on='SK_ID_CURR')


# Convert categorical features to numerical approximations (via pipeline)
class ClaimAttributesAdder(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self
    def transform(self, X, y=None): 
        charlson_idx_dt = {'0': 0, '1-2': 2, '3-4': 4, '5+': 6}
        los_dt = {'1 day': 1, '2 days': 2, '3 days': 3, '4 days': 4, '5 days': 5, '6 days': 6,
          '1- 2 weeks': 11, '2- 4 weeks': 21, '4- 8 weeks': 42, '26+ weeks': 180}
        X['PayDelay'] = X['PayDelay'].apply(lambda x: int(x) if x != '162+' else int(162))
        X['DSFS'] = X['DSFS'].apply(lambda x: None if pd.isnull(x) else int(x[0]) + 1)
        X['CharlsonIndex'] = X['CharlsonIndex'].apply(lambda x: charlson_idx_dt[x])
        X['LengthOfStay'] = X['LengthOfStay'].apply(lambda x: None if pd.isnull(x) else los_dt[x])
        return X


X_train['INCOME_CREDIT_PERCENT'] = (X_train.AMT_INCOME_TOTAL / X_train.AMT_CREDIT).replace(np.inf, 0)  
X_train['REPAYMENT_PERCENT'] = (X_train.CNT_INSTALMENT_FUTURE_max / X_train.CNT_INSTALMENT_min).replace(np.inf, 0)
X_train['ANN_INC_PERCENT'] = (X_train.AMT_ANNUITY / X_train.AMT_INCOME_TOTAL).replace(np.inf, 0)


X_kaggle_test['INCOME_CREDIT_PERCENT'] = (X_kaggle_test.AMT_INCOME_TOTAL / X_kaggle_test.AMT_CREDIT).replace(np.inf, 0)  
X_kaggle_test['REPAYMENT_PERCENT'] = (X_kaggle_test.CNT_INSTALMENT_FUTURE_max / X_kaggle_test.CNT_INSTALMENT_min).replace(np.inf, 0)
X_kaggle_test['ANN_INC_PERCENT'] = (X_kaggle_test.AMT_ANNUITY / X_kaggle_test.AMT_INCOME_TOTAL).replace(np.inf, 0)


mask = (X_train['TARGET'] == 0) & (X_train.isna().mean(axis=1) >= 0.4)

# drop rows that satisfy the condition
X_train = X_train.drop(X_train[mask].index)


X_train.shape

(287427, 178)


from copy import deepcopy


percent = (X_train.isnull().sum()/X_train.isnull().count()*100).sort_values(ascending = False).round(2)
sum_missing = X_train.isna().sum().sort_values(ascending = False)
total_count_with = X_train.isnull().count()
total_count_without = X_train.count()
missing_application_train_data  = pd.concat([percent, sum_missing, total_count_without, total_count_with], axis=1, keys=['Percent', 'Missing Count', 'Total Count without Missing value', 'Total Count'])
missing_application_train_data


X_train_missing_data = pd.DataFrame((X_train.isnull().sum())/X_train.shape[0])
X_train_missing_col = X_train_missing_data[X_train_missing_data[0] > 0.6].index
X_train = X_train[[col for col in X_train.columns if col not in X_train_missing_col]]
X_train.shape

(286483, 152)


#ref: https://stackoverflow.com/questions/29294983/how-to-calculate-correlation-between-all-columns-and-remove-highly-correlated-on

X_train_corr_matrix = X_train.corr().abs()

# Select upper triangle of correlation matrix
upper = X_train_corr_matrix.where(np.triu(np.ones(X_train_corr_matrix.shape), k=1).astype(bool))

# Find features with correlation greater than 0.85
to_drop = [column for column in upper.columns if any(upper[column] > 0.85)]

# Drop features
X_train.drop(to_drop, axis=1, inplace=True)

X_train.shape

(286483, 107)


X_train2=deepcopy(X_train)


X_train=deepcopy(X_train2)


# Split the provided training data into training and validationa and test
# The kaggle evaluation test set has no labels
#
from sklearn.model_selection import train_test_split

use_application_data_ONLY = False #use joined data
if use_application_data_ONLY:
    # just selected a few features for a baseline experiment
    selected_features1 = [col for col in datasets["application_train"].columns if col != 'TARGET']

    selected_features2= selected_features1+br_features+pr_features+cc_features+ip_features+pos_features+['NAME_CONTRACT_TYPE']
    
    selected_features3 = ['AMT_INCOME_TOTAL',  'AMT_CREDIT','DAYS_EMPLOYED','DAYS_BIRTH','EXT_SOURCE_1',
        'EXT_SOURCE_2','EXT_SOURCE_3','CODE_GENDER', 'FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE', 
                   'NAME_EDUCATION_TYPE','OCCUPATION_TYPE','NAME_INCOME_TYPE']
    
    X_train = datasets["application_train"][selected_features2]
    y_train = datasets["application_train"]['TARGET']
    X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.15, random_state=42)
    X_kaggle_test= datasets["application_test"][selected_features2]
    # y_test = datasets["application_test"]['TARGET']   #why no  TARGET?!! (hint: kaggle competition)

else: 
    y_train = X_train['TARGET']
    selected_features1 = [col for col in X_train.columns if col not in ['TARGET','SK_ID_CURR']]
    selected_features = ['AMT_INCOME_TOTAL',  'AMT_CREDIT','DAYS_EMPLOYED','DAYS_BIRTH','EXT_SOURCE_1',
            'EXT_SOURCE_2','EXT_SOURCE_3','CODE_GENDER', 'FLAG_OWN_REALTY','FLAG_OWN_CAR','NAME_CONTRACT_TYPE', 
                      'NAME_EDUCATION_TYPE','OCCUPATION_TYPE','NAME_INCOME_TYPE']
    X_train = X_train[selected_features1]
    X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, stratify=y_train,test_size=0.15, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, stratify=y_train, test_size=0.15, random_state=42)
    X_kaggle_test= X_kaggle_test[selected_features1]
# y_test = datasets["application_test"]['TARGET']   #why no  TARGET?!! (hint: kaggle competition)

    
print(f"X train           shape: {X_train.shape}")
print(f"X validation      shape: {X_valid.shape}")
print(f"X test            shape: {X_test.shape}")
print(f"X X_kaggle_test   shape: {X_kaggle_test.shape}")

X train           shape: (206983, 105)
X validation      shape: (42973, 105)
X test            shape: (36527, 105)
X X_kaggle_test   shape: (48744, 105)


X_train1=deepcopy(X_train)


from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
from datetime import datetime
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import time


from xgboost import XGBClassifier
from sklearn.ensemble import GradientBoostingClassifier

from sklearn.naive_bayes import GaussianNB
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix

from time import time
import xgboost
from xgboost import plot_importance


col=X_train1.columns

col


num_attribs1 = X_train1.select_dtypes(include=['int64', 'float64']).columns
imputer = SimpleImputer(strategy='mean')
X_train1[num_attribs1] = imputer.fit_transform(X_train1[num_attribs1])

cat_attribs1 = X_train1.select_dtypes(include=['object']).columns
label_encoder = LabelEncoder()
for col in cat_attribs1: 
    imputer = SimpleImputer(strategy='constant', fill_value='missing')
    X_train1[col] = imputer.fit_transform(X_train1[[col]])
    X_train1[col] = label_encoder.fit_transform(X_train1[col])

# Apply standard scaling
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train1)


xgb_model = XGBClassifier()
xgb_model.fit(X_train_scaled, y_train)

# Calculate feature importances
importances = xgb_model.feature_importances_


col=X_train1.columns
# create a DataFrame of feature importances
importance_df = pd.DataFrame({
    "Feature": col,
    "Importance": importances
})

# sort the DataFrame by feature importance in descending order
importance_df = importance_df.sort_values("Importance", ascending=False)
importance_df


# Sort the feature importances in ascending order
sorted_idx = importances.argsort()

# Create the bar plot
fig, ax = plt.subplots(figsize=(15, 20))
ax.barh(col[sorted_idx], importances[sorted_idx])

# Add annotations to the plot
for i, v in enumerate(importances[sorted_idx]):
    ax.text(v, i, "{:.3f}".format(v), color='blue', fontweight='bold', fontsize=10)

ax.set_xlabel("XGBoost Feature Importance")
plt.show()


threshold = 0.005
importance_df_filtered = importance_df[importance_df["Importance"] > threshold]


final_selected_feature=importance_df_filtered['Feature'].values


X_train_final=X_train[final_selected_feature]


X_test_final=X_test[final_selected_feature]


X_valid_final=X_valid[final_selected_feature]


X_kaggle_test1=deepcopy(X_kaggle_test)


X_kaggle_test=X_kaggle_test[final_selected_feature]


X_train_final.to_csv("X_train.csv")
X_test_final.to_csv("X_test.csv")
X_valid_final.to_csv("X_valid.csv")
X_kaggle_test.to_csv("X_kaggle_test.csv")


y_train.to_csv("y_train.csv")
y_test.to_csv("y_test.csv")
y_valid.to_csv("y_valid.csv")


# Create a class to select numerical or categorical columns 
# since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values


numerical_features = X_train_final.select_dtypes(include=[np.number])
categorical_features = X_train_final.select_dtypes(exclude=[np.number])

numerical_features = list(numerical_features.columns)
categorical_features = list(categorical_features.columns)

print("=" * 30)
print("|     Numerical Features     |")
print("=" * 30)
for features in numerical_features:
  print("|  " + features)
print("=" * 30)

print("\n\n")
print("=" * 30)
print("|    Categorical Features    |")
print("=" * 30)
for features in categorical_features:
  print("|  " + features)
print("=" * 30)


# Identify the numeric features we wish to consider.

num_attribs1= X_train_final.select_dtypes(include=['int64', 'float64']).columns


num_pipeline = Pipeline([
        ('selector', DataFrameSelector(num_attribs1)),
        ('imputer', SimpleImputer(strategy='mean')),
        ('std_scaler', StandardScaler()),
    ])

# Identify the categorical features we wish to consider.

cat_attribs1 = X_train_final.select_dtypes(include=['object']).columns

# Notice handle_unknown="ignore" in OHE which ignore values from the validation/test that
# do NOT occur in the training set
cat_pipeline = Pipeline([
        ('selector', DataFrameSelector(cat_attribs1)),
        #('imputer', SimpleImputer(strategy='most_frequent')),
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
    ])

data_prep_pipeline = FeatureUnion(transformer_list=[
        ("num_pipeline", num_pipeline),
        ("cat_pipeline", cat_pipeline),
    ])


from joblib import load
from joblib import dump


def train_model(model_name,model,description,hyper,saved):
    input_f=str(X_train_final.shape[1])
    model_name=model_name+"-"+input_f+" input features"
    if saved:
      # load the saved model
      pipeline = load(f'{model_name}.joblib')
    else:
        print(model_name)
        pipeline = Pipeline([
            ("preparation", data_prep_pipeline),
            ('model',model)])

        t0 = time()
        
        pipeline.fit(X_train_final, y_train)
        dump(pipeline, f'{model_name}.joblib')

        Training_time = time()-t0

    
    if hyper:
        print("Best parameters")
        best_p=pipeline.named_steps["model"].best_params_
        print(best_p)
    else:
        best_p=pipeline.named_steps["model"].get_params()
        print(best_p)
    

    y_pred_train=pipeline.predict(X_train_final)
    train_accuracy = accuracy_score(y_train, y_pred_train)
    train_r_score=roc_auc_score(y_train, pipeline.predict_proba(X_train_final)[:, 1])

    #for validation set
    y_pred_valid=pipeline.predict(X_valid_final)
    valid_accuracy = accuracy_score(y_valid, y_pred_valid)
    valid_r_score=roc_auc_score(y_valid, pipeline.predict_proba(X_valid_final)[:, 1])
    
    
    
    # Make predictions on test
    t1= time()
    y_pred_test=pipeline.predict(X_test_final)
    y_pred_model=pipeline.predict_proba(X_test_final)[:, 1]
    testing_time=time() - t1
    test_accuracy = accuracy_score(y_test, y_pred_test)
    test_r_score=roc_auc_score(y_test, pipeline.predict_proba(X_test_final)[:, 1])
    
    
    print(classification_report(y_test, y_pred_test))  

    # Create confusion matrix for y_pred_train
    cm_train = confusion_matrix(y_train, y_pred_train)

    # Create confusion matrix for y_pred_test
    cm_test = confusion_matrix(y_test, y_pred_test)


    # Visualize confusion matrix for y_pred_train
    sns.heatmap(cm_train, annot=True, cmap='Blues')
    plt.title('Confusion Matrix (Train)')
    plt.xlabel('Predicted Labels')
    plt.ylabel('True Labels')
    plt.show()

    # Visualize confusion matrix for y_pred_test
    sns.heatmap(cm_test, annot=True, cmap='Blues')
    plt.title('Confusion Matrix (Test)')
    plt.xlabel('Predicted Labels')
    plt.ylabel('True Labels')
    plt.show()




    expLog.loc[len(expLog)] =[f"{model_name}",f"{train_accuracy*100:8.2f}%",f"{valid_accuracy*100:8.2f}%",f"{test_accuracy*100:8.2f}%",\
                              f"{train_r_score*100:8.2f}%",f"{valid_r_score*100:8.2f}%",f"{test_r_score*100:8.2f}%",\
                                    f"{Training_time:8.2f} secs", f"{testing_time:8.2f} secs",
                                    f"{best_p}",description]
    return y_pred_model


expLog=pd.DataFrame(columns=["exp_name", 
                                   "Train Acc", 
                                   "Valid Acc",
                                   "Test  Acc",
                                   "Train AUC", 
                                   "Valid AUC",
                                   "Test  AUC",
                                   "Train time",
                                   "Test time",
                                   "Best Params",
                                   "Description"
                                  ])


try:
    expLog
except NameError:
    expLog = pd.DataFrame(columns=["exp_name", 
                                   "Train Acc", 
                                   "Valid Acc",
                                   "Test  Acc",
                                   "Train AUC", 
                                   "Valid AUC",
                                   "Test  AUC",
                                   "Train time",
                                   "Test time",
                                   "Best Params",
                                   "Description"
                                  ])


expLog


%%time 
np.random.seed(42)
lr_baseline_pipeline = Pipeline([
        ("preparation", data_prep_pipeline),
        ("model", LogisticRegression())
    ])
lr_base_model = lr_baseline_pipeline.fit(X_train_final, y_train)


y_pred_lr=train_model("Baseline Logisitc Regression",LogisticRegression(),"Joined Feature Aggregated in input features",False,False)


expLog

from sklearn.metrics import roc_auc_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> roc_auc_score(y_true, y_scores)
0.75


y_pred_dt=train_model("Baseline Decision Tree",DecisionTreeClassifier(random_state=42),"Joined Feature Aggregated in input features",False,False)


expLog


y_pred_rf=train_model("Baseline Random Forest",RandomForestClassifier(),"Joined Feature Aggregated in input features",False,False)


expLog


y_pred_gb=train_model("Baseline GaussianNB",GaussianNB(),"Joined Feature Aggregated in input features",False,False)


expLog


from sklearn import metrics
plt.figure(figsize=(8, 8))
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_lr)
auc = round(metrics.roc_auc_score(y_test, y_pred_lr), 4)
plt.plot(fpr,tpr,label="Logistic Regression, AUC="+str(auc))

fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_dt)
auc = round(metrics.roc_auc_score(y_test, y_pred_dt), 4)
plt.plot(fpr,tpr,label="Decision Tree Classifier, AUC="+str(auc))


fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_rf)
auc = round(metrics.roc_auc_score(y_test, y_pred_rf), 4)
plt.plot(fpr,tpr,label="Random Forest Classifier, AUC="+str(auc))

fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_gb)
auc = round(metrics.roc_auc_score(y_test, y_pred_gb), 4)
plt.plot(fpr,tpr,label="Gaussian Naive Bayes Classifier, AUC="+str(auc))

plt.legend()


y_pred_XGB=train_model("Baseline XGBboost",XGBClassifier(),"Joined Feature Aggregated in input features",False,False)


expLog


#train_model("Baseline GradientBoosting",GradientBoostingClassifier(),"Joined Feature Aggregated in input features",False)


#expLog


tune_model=LogisticRegression()

params = {'C':[0.01,10.0, 100.0], 
          'penalty':['l1', 'l2']}

clf_gridsearch = GridSearchCV(tune_model,params,cv=3,scoring='accuracy',n_jobs=-1, verbose=1)
y_pred_lr1=train_model("Tuned Logisitc Regression",clf_gridsearch,"Joined Feature Aggregated+Hyerparameters",True,False)


expLog


# Create a decision tree classifier
dt = DecisionTreeClassifier()

# Define the hyperparameter grid to search over
params = {
    'max_depth': [2, 4],
    'min_samples_split': [2, 4],
    'min_samples_leaf': [2, 4]}

clf_gridsearch = GridSearchCV(dt, params, cv=3,n_jobs=-1, verbose=1)
y_pred_dt1=train_model("Tuned Decision Tree",clf_gridsearch,"Joined Feature Aggregated+Hyerparameters",True,False)


expLog


rf = RandomForestClassifier()

# Define the hyperparameter grid to search over
params = {
    'n_estimators': [10,50],
    'min_samples_leaf': [2, 5, 10],
    'max_depth': [5, 10]  
}
clf_gridsearch = GridSearchCV(rf, params, cv=3,n_jobs=-1, verbose=1)
y_pred_rf1=train_model("Tuned Random Forest Trees",clf_gridsearch,"Joined Feature Aggregated+Hyerparameters",True,False)


expLog


from sklearn import metrics
plt.figure(figsize=(10, 5))
fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_lr1)
auc = round(metrics.roc_auc_score(y_test, y_pred_lr), 4)
plt.plot(fpr,tpr,label="Tuned Logistic Regression, AUC="+str(auc))

fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_dt1)
auc = round(metrics.roc_auc_score(y_test, y_pred_dt), 4)
plt.plot(fpr,tpr,label="Tuned Decision Tree Classifier, AUC="+str(auc))


fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_rf1)
auc = round(metrics.roc_auc_score(y_test, y_pred_rf), 4)
plt.plot(fpr,tpr,label="Tuned Random Forest Classifier, AUC="+str(auc))

fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_XGB)
auc = round(metrics.roc_auc_score(y_test, y_pred_XGB), 4)
plt.plot(fpr,tpr,label="XGBoost Classifier, AUC="+str(auc))

plt.legend()


from sklearn.neural_network import MLPClassifier


y_pred_mlp=train_model("Baseline MLP",MLPClassifier(),"Joined Feature Aggregated",False,False)


expLog


mlp = MLPClassifier()
param_grid = {
    'hidden_layer_sizes': [(120, 80, 40), (150, 100, 50), (128, 64, 32)],
    'max_iter': [5, 10, 50, 100, 150, 200],
    'activation': ['tanh', 'relu', 'sigmoid'],
    'solver': ['sgd', 'adam'],
    'alpha': [0.1, 0.01, 0.001, 0.0001, 0.5, 0.05],
    'learning_rate': ['constant','adaptive'],
}
clf_gridsearch = GridSearchCV(mlp, params, cv=3, n_jobs=-1, verbose=1)
train_model("Tuned MLP Classifier",clf_gridsearch,"Joined Feature Aggregated+Hyperparameter Tuning")


expLog


from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive


import numpy as np
import pandas as pd 
from sklearn.preprocessing import LabelEncoder
import os
import zipfile
from sklearn.base import BaseEstimator, TransformerMixin
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline, FeatureUnion
from pandas.plotting import scatter_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_auc_score
import warnings
warnings.filterwarnings('ignore')


import torch
import torch.utils.data
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset


import time
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from collections import Counter



torch.manual_seed(0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from torch.utils.data.sampler import WeightedRandomSampler


from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter("runs/exp4")


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%load_ext tensorboard


#Read the saved filtered datasets
X_train= pd.read_csv('X_train.csv')
X_valid = pd.read_csv('X_valid.csv')
X_test = pd.read_csv('X_test.csv')
X_kaggle_test = pd.read_csv('X_kaggle_test.csv')


y_train=pd.read_csv('y_train.csv')
y_valid=pd.read_csv('y_valid.csv')
y_test=pd.read_csv('y_test.csv')


#Remove Index Column 
X_train= X_train.iloc[:,1:]
X_valid =X_valid.iloc[:,1:]
X_test = X_test.iloc[:,1:]
X_kaggle_test = X_kaggle_test.iloc[:,1:]


y_train=y_train['TARGET']
y_valid=y_valid['TARGET']
y_test=y_test['TARGET']


#WHILE EVAULATING FOR X_KAGGLE_TEST, WE ENCOUNTERED SHAPE DIFFERENCES BECAUSE OF FOLLOWING COLUMNS'S VALUE AS THEY DIDN'T EXIST IN KAGGLE TEST.
#WE DECIDED TO REMOVE THOSE ROWS WHICH DOESN'T CONTAIN THE FOLLOWING CONDITION (THEY ARE ONLY 3-4 ROWS THAT GOT REMOVED)
# Define the conditions for filtering out rows
cond1 = (X_train['CODE_GENDER'] != 'XNA') & (X_train['NAME_INCOME_TYPE'] != 'Maternity leave' ) & (X_train['NAME_FAMILY_STATUS'] != 'Unknown' ) 
cond2 = (X_valid['CODE_GENDER'] != 'XNA') & (X_valid['NAME_INCOME_TYPE'] != 'Maternity leave') & (X_valid['NAME_FAMILY_STATUS'] != 'Unknown' ) 
cond3 = (X_test['CODE_GENDER'] != 'XNA') & (X_test['NAME_INCOME_TYPE'] != 'Maternity leave') & (X_test['NAME_FAMILY_STATUS'] != 'Unknown' ) 

# Filter out rows that meet the conditions
X_train = X_train.loc[cond1]
X_valid = X_valid.loc[cond2]
X_test = X_test.loc[cond3]

# Remove the corresponding rows from the target variable
y_train = y_train.loc[cond1]
y_valid = y_valid.loc[cond2]
y_test = y_test.loc[cond3]


#CONVERT THEM TO NUMPY ARRAY FOR TRAINING
y_train = y_train.to_numpy()
y_validation = y_valid.to_numpy()
y_test = y_test.to_numpy()


#Tranform the features again for Pytorch model
# Create a class to select numerical or categorical columns 
# since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

# Identify the numeric features we wish to consider.

num_attribs1= X_train.select_dtypes(include=['int64', 'float64']).columns


num_pipeline = Pipeline([
        ('selector', DataFrameSelector(num_attribs1)),
        ('imputer', SimpleImputer(strategy='mean')),
        ('std_scaler', StandardScaler()),
    ])

# Identify the categorical features we wish to consider.

cat_attribs1 = X_train.select_dtypes(include=['object']).columns

# Notice handle_unknown="ignore" in OHE which ignore values from the validation/test that
# do NOT occur in the training set
cat_pipeline = Pipeline([
        ('selector', DataFrameSelector(cat_attribs1)),
        #('imputer', SimpleImputer(strategy='most_frequent')),
        ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
        ('ohe', OneHotEncoder(sparse=False, handle_unknown="ignore"))
    ])

data_prep_pipeline = FeatureUnion(transformer_list=[
        ("num_pipeline", num_pipeline),
        ("cat_pipeline", cat_pipeline),
    ])

X_train = data_prep_pipeline.fit_transform(X_train)
X_validation = data_prep_pipeline.transform(X_valid) 
X_test = data_prep_pipeline.transform(X_test)


# # define undersample strategy
# random_undersampler = RandomUnderSampler(sampling_strategy='majority')
# # fit and apply the transform
# X_train, y_train = random_undersampler.fit_resample(X_train, y_train)


# # Apply SMOTE
# sm = SMOTE(random_state=42)
# X_train, y_train = sm.fit_resample(X_train,y_train)


# Define the model architecture
class BinaryClassifier(nn.Module):
    def __init__(self, input_size):
        super(BinaryClassifier, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 16)
        self.fc4 = nn.Linear(16, 1)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.dropout = nn.Dropout(p=0.2)

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.relu(self.fc3(x))
        x = self.dropout(x)
        x = self.sigmoid(self.fc4(x))
        return x

# Define hyperparameters
batch_size = 128
learning_rate = 0.001

# Convert data to PyTorch tensors and create DataLoader
X_train_tensor = torch.from_numpy(X_train).float()
X_valid_tensor = torch.from_numpy(X_validation).float()
X_test_tensor = torch.from_numpy(X_test).float()
y_train_tensor = torch.from_numpy(y_train).float()
y_valid_tensor = torch.from_numpy(y_validation).float()
y_test_tensor = torch.from_numpy(y_test).float()

train_ds = TensorDataset(X_train_tensor, y_train_tensor)
valid_ds = TensorDataset(X_valid_tensor, y_valid_tensor)
test_ds = TensorDataset(X_test_tensor, y_test_tensor)



train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(valid_ds, batch_size=batch_size)
test_loader = DataLoader(test_ds, batch_size=batch_size)

# Initialize the model and optimizer
model = BinaryClassifier(X_train.shape[1])
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)


model

BinaryClassifier(
  (fc1): Linear(in_features=199, out_features=64, bias=True)
  (fc2): Linear(in_features=64, out_features=32, bias=True)
  (fc3): Linear(in_features=32, out_features=16, bias=True)
  (fc4): Linear(in_features=16, out_features=1, bias=True)
  (relu): ReLU()
  (sigmoid): Sigmoid()
  (dropout): Dropout(p=0.2, inplace=False)
)


time1=time.time()
epochs = 20
# Train the model
try:
      for epoch in range(epochs):
          train_loss = 0
          train_acc = 0
          train_auc=0
          valid_loss = 0
          valid_acc = 0
          valid_auc=0

          pred=[]
          epoch_label=[]

          # Training loop
          model.train()
          count=0
          for X, y in train_loader:
              optimizer.zero_grad()
              y_pred = model(X.float())
              
              loss = criterion(y_pred.squeeze(), y)

              loss.backward()
              optimizer.step()
              y_pred=torch.round(y_pred)
              
              train_loss += loss.item()
              train_acc += (y_pred.float()  == y).float().mean().item()
              train_auc += roc_auc_score(y.tolist(), y_pred.tolist())
            
              count += 1

          train_loss = np.round(train_loss/count, 3)
          train_acc=np.round(train_acc/count,3)
          train_auc=np.round(train_auc/count,3)
          writer.add_scalar('Training Auc',train_auc,epoch)
          writer.add_scalar('Training Loss',train_loss,epoch)
          #Validation loop
          model.eval()
          count=0
          with torch.no_grad():
              for X, y in valid_loader:
                  y_pred = model(X)
                  
                  loss = criterion(y_pred.squeeze(), y)
                  y_pred=torch.round(y_pred)
                  valid_loss += loss.item()
                  valid_acc += (y_pred.float()== y).float().mean().item()
                  valid_auc += roc_auc_score(y.tolist(), y_pred.tolist())
                  count += 1

          valid_loss = np.round(valid_loss/count, 3)
          valid_acc=np.round(valid_acc/count,3)
          valid_auc=np.round(valid_auc/count,3)

          writer.add_scalar('Validation Auc',valid_auc,epoch)
        
          
          # Print the results for this epoch
          print(f'Epoch {epoch + 1}, Train Loss: {train_loss}, '
                f'Train AUC: {train_auc}, '
                f'Valid Loss: {valid_loss}, '
                f'Valid AUC: {valid_auc}')
      total_time=time.time()-time1  
      #print((epoch,loss, accuracy, auc))
      # Set the model to evaluation mode
      model.eval()


      y_pred = []
      y_true = []

      # Iterate over the test set and predict the probabilities and true labels
      with torch.no_grad():
          for inputs, labels in test_loader:
              outputs = model(inputs)
              y_pred.extend(outputs.squeeze())
              y_true.extend(labels.numpy())

      # Calculate the ROC AUC score
      test_auc = roc_auc_score(y_true, y_pred)
except:
  pass

Epoch 1, Train Loss: 0.273, Train AUC: 0.5, Valid Loss: 0.258, Valid AUC: 0.5
Epoch 2, Train Loss: 0.261, Train AUC: 0.504, Valid Loss: 0.256, Valid AUC: 0.505
Epoch 3, Train Loss: 0.257, Train AUC: 0.516, Valid Loss: 0.256, Valid AUC: 0.515
Epoch 4, Train Loss: 0.253, Train AUC: 0.529, Valid Loss: 0.25, Valid AUC: 0.529
Epoch 5, Train Loss: 0.249, Train AUC: 0.542, Valid Loss: 0.25, Valid AUC: 0.531
Epoch 6, Train Loss: 0.246, Train AUC: 0.545, Valid Loss: 0.248, Valid AUC: 0.536
Epoch 7, Train Loss: 0.244, Train AUC: 0.549, Valid Loss: 0.248, Valid AUC: 0.541
Epoch 8, Train Loss: 0.242, Train AUC: 0.555, Valid Loss: 0.251, Valid AUC: 0.545
Epoch 9, Train Loss: 0.241, Train AUC: 0.558, Valid Loss: 0.251, Valid AUC: 0.548
Epoch 10, Train Loss: 0.239, Train AUC: 0.562, Valid Loss: 0.25, Valid AUC: 0.547
Epoch 11, Train Loss: 0.237, Train AUC: 0.565, Valid Loss: 0.254, Valid AUC: 0.542
Epoch 12, Train Loss: 0.236, Train AUC: 0.567, Valid Loss: 0.253, Valid AUC: 0.553
Epoch 13, Train Loss: 0.235, Train AUC: 0.571, Valid Loss: 0.254, Valid AUC: 0.545
Epoch 14, Train Loss: 0.233, Train AUC: 0.573, Valid Loss: 0.254, Valid AUC: 0.548
Epoch 15, Train Loss: 0.232, Train AUC: 0.577, Valid Loss: 0.256, Valid AUC: 0.547
Epoch 16, Train Loss: 0.231, Train AUC: 0.577, Valid Loss: 0.259, Valid AUC: 0.551
Epoch 17, Train Loss: 0.229, Train AUC: 0.584, Valid Loss: 0.26, Valid AUC: 0.557
Epoch 18, Train Loss: 0.229, Train AUC: 0.585, Valid Loss: 0.261, Valid AUC: 0.558
Epoch 19, Train Loss: 0.227, Train AUC: 0.588, Valid Loss: 0.266, Valid AUC: 0.559
Epoch 20, Train Loss: 0.226, Train AUC: 0.592, Valid Loss: 0.261, Valid AUC: 0.552


try: hcdrLog 
except : hcdrLog = pd.DataFrame(
    columns=[
        "Architecture string", 
        "Optimizer", 
        "Epochs", 
        "Train AUC",
        "Valid Auc",
        "Test AUC",
        "Total experiment time"
    ]
)


hcdrLog


hcdrLog.loc[len(hcdrLog)] = [
    '199-relu-64-relu-32-relu-16-dropout-sigmoid-1', 
    "adam", 
    f"{epochs}", 
    f"{train_auc*100}%",
    f"{valid_auc*100}%",
    f"{test_auc*100}%",
    f"{total_time:8.2f} secs",

]

hcdrLog


%tensorboard --logdir=runs/exp4 --host=127.0.0.3

SK_ID_CURR,TARGET
100001,0.1
100005,0.9
100013,0.2
etc.


X_kaggle_test= data_prep_pipeline.fit_transform(X_kaggle_test)

# Set the model to evaluation mode
model.eval()

# Convert the Kaggle test data to a tensor and move it to the device
X_kaggle_test_tensor = torch.from_numpy(X_kaggle_test).float()

# Generate predictions for the Kaggle test set
with torch.no_grad():
    kaggle_preds = model(X_kaggle_test_tensor)
    
# Extract the predicted probabilities for the positive class
kaggle_preds = kaggle_preds.numpy()


dataset=pd.read_csv("/content/drive/MyDrive/AML/application_test.csv")


# Submission dataframe
submit_df = dataset[['SK_ID_CURR']]
submit_df['TARGET'] = kaggle_preds

submit_df.head()


submit_df.to_csv("MLP_submission5.csv",index=False)


! kaggle competitions submit -c home-credit-default-risk -f MLP_submission5.csv -m "MLP_submission5"

100% 876k/876k [00:03<00:00, 254kB/s]
Successfully submitted to Home Credit Default Risk

Phase	Phase Leader	Tasks
Phase 1	Shyam Makwana	Project proposal, Layout, Notebook, Credit assignment plan, Gantt chart, Dataset
Phase 2	Aarushi Dua	Grab data, EDA, Metrics, Baseline models, Baseline pipeline, Brief report, Presentation and script
Phase 3	Sai Teja Burla	Feature engineering, Hyperparameter tuning, Additional feature selection, ensemble methods, Presentation and script
Phase 4	Lakshay Madaan	Neural Network, Advanced model architectures, Loss functions, Final project presentation and report

Phase	Member Name	Task Name	Description
Phase 1	Aarushi Dua Sai Teja Burla Shyam Makwana Lakshay Madaan	Problem Understanding	Comprehending the problem and understanding the provided data
	Shyam Makwana	Assigning Tasks	Assigning tasks to each of the team members
		Phase Planning	Planning all the phases
		Data Description	Describing what information each of the given csv files have with a sample of their content
	Sai Teja Burla	Abstract	Brief description of the problem statement at hand
		Researched ML models and metric	Description of ML Models and Evaluation Metrics
		Block Diagram	Visual representation of the pipeline
	Aarushi Dua	Model Pipeline	Explained the steps to perform during the course of the project
	Aarushi Dua	Gantt Chart	Summarize the timeline of the project using gantt charts
	Aarushi Dua Sai Teja Burla	Credit Assignment Plan	Design the four phases and describe the task required.
	Lakshay Madaan	Environmental Setup	Connected Data with docker
	Lakshay Madaan	Data Preparation	Checked Dataset are loading and performing basic data visualization.
Phase 2	Aarushi Dua Sai Teja Burla	Data Visualization and EDA	Visualize the dataset to gather insights and perform different data pre-processing techniques.
	Lakshay Madaan Shyam Makwana	Feature Engineering and Pipeline Creation	Data Transformation is required and will add/remove necessary features. Along with creating a pipeline for numeric and categorical features.
	Aarushi Dua	Model Training	Fit the training data on various ML algorithms using pipelines
	Sai Teja Burla	Model Evaluation	Apply different evaluation metrics to get the accuracy and loss
	Shyam Makwana	Research about different Hyperparameters	Gather hyperparameters of each model applied into the GridsearchCV
	Lakshay Madaan	Comparing Baseline Models	Compare the results of each baseline model.
Phase 3	Sai Teja Burla	Feature Importance	Figure out the importance of each features used
	Aarushi Dua	Tuning Hyperparameters	Hyperparameter tuning is performed again to improve the performance.
	Shyam Makhwana	Visualizing different algorithm’s results	Plot bars and charts to display the results of each model
	Lakshay Madaan	Feature Engineering	Any further manipulation in features if required
Phase 4	Lakshay Madaan	Final Model Evaluation	Perform final model evaluation on the best model achieved.
	Shyam Makhwana	Report Writing and Video Editing	Write report for final submission and record the video presentation
	Sai Teja Burla Aarushi Dua	Adding Multilayer Perceptron	Introduce advance ML model; i.e Pytorch model for further improvement in model’s accuracy

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	351000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	-9461	-637	-3648.0	-2120	NaN	1	1	0	1	1	Laborers	1.0	2	2	WEDNESDAY	10	0	0	Business Entity Type 3	0.083037	0.262949	0.139376	0.0247	0.0369	0.9722	0.6192	0.0143	0.00	0.0690	0.0833	0.1250	0.0369	0.0202	0.0190	0.0000	0.0000	0.0252	0.0383	0.9722	0.6341	0.0144	0.0000	0.0690	0.0833	0.1250	0.0377	0.022	0.0198	0.0	0.0	0.0250	0.0369	0.9722	0.6243	0.0144	0.00	0.0690	0.0833	0.1250	0.0375	0.0205	0.0193	0.0000	0.00	reg oper account	block of flats	0.0149	Stone, brick	No	2.0	2.0	2.0	2.0	-1134.0	1	0	0.0	0.0	0.0	0.0	0.0	1.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	NaN	1	1	0	1	1	Core staff	2.0	1	1	MONDAY	11	0	0	School	0.311267	0.622246	NaN	0.0959	0.0529	0.9851	0.7960	0.0605	0.08	0.0345	0.2917	0.3333	0.0130	0.0773	0.0549	0.0039	0.0098	0.0924	0.0538	0.9851	0.8040	0.0497	0.0806	0.0345	0.2917	0.3333	0.0128	0.079	0.0554	0.0	0.0	0.0968	0.0529	0.9851	0.7987	0.0608	0.08	0.0345	0.2917	0.3333	0.0132	0.0787	0.0558	0.0039	0.01	reg oper account	block of flats	0.0714	Block	No	1.0	0.0	1.0	0.0	-828.0	1	0	0.0	0.0	0.0	0.0	0.0	0.0
2	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	135000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	-19046	-225	-4260.0	-2531	26.0	1	1	1	1	1	Laborers	1.0	2	2	MONDAY	9	0	0	Government	NaN	0.555912	0.729567	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-815.0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	NaN	1	1	0	1	0	Laborers	2.0	2	2	WEDNESDAY	17	0	0	Business Entity Type 3	NaN	0.650442	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	0.0	2.0	0.0	-617.0	1	0	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.0	513000.0	21865.5	513000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.028663	-19932	-3038	-4311.0	-3458	NaN	1	1	0	1	0	Core staff	1.0	2	2	THURSDAY	11	1	1	Religion	NaN	0.322738	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-1106.0	0	1	0.0	0.0	0.0	0.0	0.0	0.0

	SK_ID_CURR	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100001	Cash loans	F	N	Y	0	135000.0	568800.0	20560.5	450000.0	Unaccompanied	Working	Higher education	Married	House / apartment	0.018850	-19241	-2329	-5170.0	-812	NaN	1	1	0	1	0	1	NaN	2.0	2	2	TUESDAY	18	0	0	Kindergarten	0.752614	0.789654	0.159520	0.0660	0.0590	0.9732	NaN	NaN	NaN	0.1379	0.125	NaN	NaN	NaN	0.0505	NaN	NaN	0.0672	0.0612	0.9732	NaN	NaN	NaN	0.1379	0.125	NaN	NaN	NaN	0.0526	NaN	NaN	0.0666	0.0590	0.9732	NaN	NaN	NaN	0.1379	0.125	NaN	NaN	NaN	0.0514	NaN	NaN	NaN	block of flats	0.0392	Stone, brick	No	-1740.0	1	0	0.0	0.0	0.0	0.0	0.0	0.0
1	100005	Cash loans	M	N	Y	0	99000.0	222768.0	17370.0	180000.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.035792	-18064	-4469	-9118.0	-1623	NaN	1	1	0	1	0	0	Low-skill Laborers	2.0	2	2	FRIDAY	9	0	0	Self-employed	0.564990	0.291656	0.432962	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	1	0	0.0	0.0	0.0	0.0	0.0	3.0
2	100013	Cash loans	M	Y	Y	0	202500.0	663264.0	69777.0	630000.0	NaN	Working	Higher education	Married	House / apartment	0.019101	-20038	-4458	-2175.0	-3503	5.0	1	1	0	1	0	0	Drivers	2.0	2	2	MONDAY	14	0	0	Transport: type 3	NaN	0.699787	0.610991	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-856.0	0	1	0.0	0.0	0.0	0.0	1.0	4.0
3	100028	Cash loans	F	N	Y	2	315000.0	1575000.0	49018.5	1575000.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.026392	-13976	-1866	-2000.0	-4208	NaN	1	1	0	1	1	0	Sales staff	4.0	2	2	WEDNESDAY	11	0	0	Business Entity Type 3	0.525734	0.509677	0.612704	0.3052	0.1974	0.9970	0.9592	0.1165	0.32	0.2759	0.375	0.0417	0.2042	0.2404	0.3673	0.0386	0.08	0.3109	0.2049	0.9970	0.9608	0.1176	0.3222	0.2759	0.375	0.0417	0.2089	0.2626	0.3827	0.0389	0.0847	0.3081	0.1974	0.9970	0.9597	0.1173	0.32	0.2759	0.375	0.0417	0.2078	0.2446	0.3739	0.0388	0.0817	reg oper account	block of flats	0.3700	Panel	No	-1805.0	1	0	0.0	0.0	0.0	0.0	0.0	3.0
4	100038	Cash loans	M	Y	N	1	180000.0	625500.0	32067.0	625500.0	Unaccompanied	Working	Secondary / secondary special	Married	House / apartment	0.010032	-13040	-2191	-4000.0	-4262	16.0	1	1	1	1	0	0	NaN	3.0	2	2	FRIDAY	5	1	1	Business Entity Type 3	0.202145	0.425687	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-821.0	1	0	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_CURR	TARGET	NAME_CONTRACT_TYPE	CODE_GENDER	FLAG_OWN_CAR	FLAG_OWN_REALTY	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	NAME_TYPE_SUITE	NAME_INCOME_TYPE	NAME_EDUCATION_TYPE	NAME_FAMILY_STATUS	NAME_HOUSING_TYPE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	OCCUPATION_TYPE	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	ORGANIZATION_TYPE	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	FONDKAPREMONT_MODE	HOUSETYPE_MODE	TOTALAREA_MODE	WALLSMATERIAL_MODE	EMERGENCYSTATE_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_3	FLAG_DOCUMENT_8	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
0	100002	1	Cash loans	M	N	Y	202500.0	406597.5	24700.5	351000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.018801	-9461	-637	-3648.0	-2120	NaN	1	1	0	1	1	Laborers	1.0	2	2	WEDNESDAY	10	0	0	Business Entity Type 3	0.083037	0.262949	0.139376	0.0247	0.0369	0.9722	0.6192	0.0143	0.00	0.0690	0.0833	0.1250	0.0369	0.0202	0.0190	0.0000	0.0000	0.0252	0.0383	0.9722	0.6341	0.0144	0.0000	0.0690	0.0833	0.1250	0.0377	0.022	0.0198	0.0	0.0	0.0250	0.0369	0.9722	0.6243	0.0144	0.00	0.0690	0.0833	0.1250	0.0375	0.0205	0.0193	0.0000	0.00	reg oper account	block of flats	0.0149	Stone, brick	No	2.0	2.0	2.0	2.0	-1134.0	1	0	0.0	0.0	0.0	0.0	0.0	1.0
1	100003	0	Cash loans	F	N	N	270000.0	1293502.5	35698.5	1129500.0	Family	State servant	Higher education	Married	House / apartment	0.003541	-16765	-1188	-1186.0	-291	NaN	1	1	0	1	1	Core staff	2.0	1	1	MONDAY	11	0	0	School	0.311267	0.622246	NaN	0.0959	0.0529	0.9851	0.7960	0.0605	0.08	0.0345	0.2917	0.3333	0.0130	0.0773	0.0549	0.0039	0.0098	0.0924	0.0538	0.9851	0.8040	0.0497	0.0806	0.0345	0.2917	0.3333	0.0128	0.079	0.0554	0.0	0.0	0.0968	0.0529	0.9851	0.7987	0.0608	0.08	0.0345	0.2917	0.3333	0.0132	0.0787	0.0558	0.0039	0.01	reg oper account	block of flats	0.0714	Block	No	1.0	0.0	1.0	0.0	-828.0	1	0	0.0	0.0	0.0	0.0	0.0	0.0
2	100004	0	Revolving loans	M	Y	Y	67500.0	135000.0	6750.0	135000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.010032	-19046	-225	-4260.0	-2531	26.0	1	1	1	1	1	Laborers	1.0	2	2	MONDAY	9	0	0	Government	NaN	0.555912	0.729567	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-815.0	0	0	0.0	0.0	0.0	0.0	0.0	0.0
3	100006	0	Cash loans	F	N	Y	135000.0	312682.5	29686.5	297000.0	Unaccompanied	Working	Secondary / secondary special	Civil marriage	House / apartment	0.008019	-19005	-3039	-9833.0	-2437	NaN	1	1	0	1	0	Laborers	2.0	2	2	WEDNESDAY	17	0	0	Business Entity Type 3	NaN	0.650442	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.0	0.0	2.0	0.0	-617.0	1	0	NaN	NaN	NaN	NaN	NaN	NaN
4	100007	0	Cash loans	M	N	Y	121500.0	513000.0	21865.5	513000.0	Unaccompanied	Working	Secondary / secondary special	Single / not married	House / apartment	0.028663	-19932	-3038	-4311.0	-3458	NaN	1	1	0	1	0	Core staff	1.0	2	2	THURSDAY	11	1	1	Religion	NaN	0.322738	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0	0.0	0.0	0.0	-1106.0	0	1	0.0	0.0	0.0	0.0	0.0	0.0

	SK_ID_CURR	SK_ID_BUREAU	CREDIT_ACTIVE	CREDIT_CURRENCY	DAYS_CREDIT	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	CREDIT_TYPE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
0	215354	5714462	Closed	currency 1	-497	-153.0	-153.0	NaN	91323.0	0.0	NaN	Consumer credit	-131	NaN
1	215354	5714463	Active	currency 1	-208	1075.0	NaN	NaN	225000.0	171342.0	NaN	Credit card	-20	NaN
2	215354	5714464	Active	currency 1	-203	528.0	NaN	NaN	464323.5	NaN	NaN	Consumer credit	-16	NaN
3	215354	5714465	Active	currency 1	-203	NaN	NaN	NaN	90000.0	NaN	NaN	Credit card	-16	NaN
4	215354	5714466	Active	currency 1	-629	1197.0	NaN	77674.5	2700000.0	NaN	NaN	Consumer credit	-21	NaN

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	AMT_PAYMENT_CURRENT	AMT_PAYMENT_TOTAL_CURRENT	AMT_RECEIVABLE_PRINCIPAL	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	NAME_CONTRACT_STATUS
0	2562384	378907	-6	56.970	135000	0.0	877.5	877.5	1700.325	1800.0	1800.0	0.000	0.000	0.000	0.0	1	1.0	35.0	Active
1	2582071	363914	-1	63975.555	45000	2250.0	2250.0	0.0	2250.000	2250.0	2250.0	60175.080	64875.555	64875.555	1.0	1	0.0	69.0	Active
2	1740877	371185	-7	31815.225	450000	0.0	0.0	0.0	2250.000	2250.0	2250.0	26926.425	31460.085	31460.085	0.0	0	0.0	30.0	Active
3	1389973	337855	-4	236572.110	225000	2250.0	2250.0	0.0	11795.760	11925.0	11925.0	224949.285	233048.970	233048.970	1.0	1	0.0	10.0	Active
4	1891521	126868	-1	453919.455	450000	0.0	11547.0	11547.0	22924.890	27000.0	27000.0	443044.395	453919.455	453919.455	0.0	1	1.0	101.0	Active

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
0	1054186	161674	1.0	6	-1180.0	-1187.0	6948.360	6948.360
1	1330831	151639	0.0	34	-2156.0	-2156.0	1716.525	1716.525
2	2085231	193053	2.0	1	-63.0	-63.0	25425.000	25425.000
3	2452527	199697	1.0	3	-2418.0	-2426.0	24350.130	24350.130
4	2714724	167756	1.0	2	-1383.0	-1366.0	2165.040	2160.585

	SK_ID_PREV	SK_ID_CURR	NAME_CONTRACT_TYPE	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	WEEKDAY_APPR_PROCESS_START	HOUR_APPR_PROCESS_START	FLAG_LAST_APPL_PER_CONTRACT	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	NAME_CASH_LOAN_PURPOSE	NAME_CONTRACT_STATUS	DAYS_DECISION	NAME_PAYMENT_TYPE	CODE_REJECT_REASON	NAME_TYPE_SUITE	NAME_CLIENT_TYPE	NAME_GOODS_CATEGORY	NAME_PORTFOLIO	NAME_PRODUCT_TYPE	CHANNEL_TYPE	SELLERPLACE_AREA	NAME_SELLER_INDUSTRY	CNT_PAYMENT	NAME_YIELD_GROUP	PRODUCT_COMBINATION	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	2030495	271877	Consumer loans	1730.430	17145.0	17145.0	0.0	17145.0	SATURDAY	15	Y	1	0.0	0.182832	0.867336	XAP	Approved	-73	Cash through the bank	XAP	NaN	Repeater	Mobile	POS	XNA	Country-wide	35	Connectivity	12.0	middle	POS mobile with interest	365243.0	-42.0	300.0	-42.0	-37.0	0.0
1	2802425	108129	Cash loans	25188.615	607500.0	679671.0	NaN	607500.0	THURSDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-164	XNA	XAP	Unaccompanied	Repeater	XNA	Cash	x-sell	Contact center	-1	XNA	36.0	low_action	Cash X-Sell: low	365243.0	-134.0	916.0	365243.0	365243.0	1.0
2	2523466	122040	Cash loans	15060.735	112500.0	136444.5	NaN	112500.0	TUESDAY	11	Y	1	NaN	NaN	NaN	XNA	Approved	-301	Cash through the bank	XAP	Spouse, partner	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	high	Cash X-Sell: high	365243.0	-271.0	59.0	365243.0	365243.0	1.0
3	2819243	176158	Cash loans	47041.335	450000.0	470790.0	NaN	450000.0	MONDAY	7	Y	1	NaN	NaN	NaN	XNA	Approved	-512	Cash through the bank	XAP	NaN	Repeater	XNA	Cash	x-sell	Credit and cash offices	-1	XNA	12.0	middle	Cash X-Sell: middle	365243.0	-482.0	-152.0	-182.0	-177.0	1.0
4	1784265	202054	Cash loans	31924.395	337500.0	404055.0	NaN	337500.0	THURSDAY	9	Y	1	NaN	NaN	NaN	Repairs	Refused	-781	Cash through the bank	HC	NaN	Repeater	XNA	Cash	walk-in	Credit and cash offices	-1	XNA	24.0	high	Cash Street: high	NaN	NaN	NaN	NaN	NaN	NaN

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	NAME_CONTRACT_STATUS
0	1803195	182943	-31	48.0	45.0	Active
1	1715348	367990	-33	36.0	35.0	Active
2	1784872	397406	-32	12.0	9.0	Active
3	1903291	269225	-35	48.0	42.0	Active
4	2341044	334279	-35	36.0	35.0	Active

	SK_ID_CURR	TARGET	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	307511.000000	307511.000000	307511.000000	3.075110e+05	3.075110e+05	307499.000000	3.072330e+05	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	104582.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307509.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	134133.000000	3.068510e+05	246546.000000	151450.00000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	151450.000000	127568.000000	157504.000000	103023.000000	92646.000000	143620.000000	152683.000000	154491.000000	98869.000000	124921.000000	97312.000000	153161.000000	93997.000000	137829.000000	159080.000000	306490.000000	306490.000000	306490.000000	306490.000000	307510.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.00000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000
mean	278180.518577	0.080729	0.417052	1.687979e+05	5.990260e+05	27108.573909	5.383962e+05	0.020868	-16036.995067	63815.045904	-4986.120328	-2994.202373	12.061091	0.999997	0.819889	0.199368	0.998133	0.281066	0.056720	2.152665	2.052463	2.031521	12.063419	0.015144	0.050769	0.040659	0.078173	0.230454	0.179555	0.502130	5.143927e-01	0.510853	0.11744	0.088442	0.977735	0.752471	0.044621	0.078942	0.149725	0.226282	0.231894	0.066333	0.100775	0.107399	0.008809	0.028358	0.114231	0.087543	0.977065	0.759637	0.042553	0.074490	0.145193	0.222315	0.228058	0.064958	0.105645	0.105975	0.008076	0.027022	0.117850	0.087955	0.977752	0.755746	0.044595	0.078078	0.149213	0.225897	0.231625	0.067169	0.101954	0.108607	0.008651	0.028236	0.102547	1.422245	0.143421	1.405292	0.100049	-962.858788	0.000042	0.710023	0.000081	0.015115	0.088055	0.000192	0.081376	0.003896	0.000023	0.003912	0.000007	0.003525	0.002936	0.00121	0.009928	0.000267	0.008130	0.000595	0.000507	0.000335	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974
std	102790.175348	0.272419	0.722121	2.371231e+05	4.024908e+05	14493.737315	3.694465e+05	0.013831	4363.988632	141275.766519	3522.886321	1509.450419	11.944812	0.001803	0.384280	0.399526	0.043164	0.449521	0.231307	0.910682	0.509034	0.502737	3.265832	0.122126	0.219526	0.197499	0.268444	0.421124	0.383817	0.211062	1.910602e-01	0.194844	0.10824	0.082438	0.059223	0.113280	0.076036	0.134576	0.100049	0.144641	0.161380	0.081184	0.092576	0.110565	0.047732	0.069523	0.107936	0.084307	0.064575	0.110111	0.074445	0.132256	0.100977	0.143709	0.161160	0.081750	0.097880	0.111845	0.046276	0.070254	0.109076	0.082179	0.059897	0.112066	0.076144	0.134467	0.100368	0.145067	0.161934	0.082167	0.093642	0.112260	0.047415	0.070166	0.107462	2.400989	0.446698	2.379803	0.362291	826.808487	0.006502	0.453752	0.009016	0.122010	0.283376	0.013850	0.273412	0.062295	0.004771	0.062424	0.002550	0.059268	0.054110	0.03476	0.099144	0.016327	0.089798	0.024387	0.022518	0.018299	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295
min	100002.000000	0.000000	0.000000	2.565000e+04	4.500000e+04	1615.500000	4.050000e+04	0.000290	-25229.000000	-17912.000000	-24672.000000	-7197.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014568	8.173617e-08	0.000527	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-4292.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	189145.500000	0.000000	0.000000	1.125000e+05	2.700000e+05	16524.000000	2.385000e+05	0.010006	-19682.000000	-2760.000000	-7479.500000	-4299.000000	5.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.334007	3.924574e-01	0.370650	0.05770	0.044200	0.976700	0.687200	0.007800	0.000000	0.069000	0.166700	0.083300	0.018700	0.050400	0.045300	0.000000	0.000000	0.052500	0.040700	0.976700	0.699400	0.007200	0.000000	0.069000	0.166700	0.083300	0.016600	0.054200	0.042700	0.000000	0.000000	0.058300	0.043700	0.976700	0.691400	0.007900	0.000000	0.069000	0.166700	0.083300	0.018700	0.051300	0.045700	0.000000	0.000000	0.041200	0.000000	0.000000	0.000000	0.000000	-1570.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	278202.000000	0.000000	0.000000	1.471500e+05	5.135310e+05	24903.000000	4.500000e+05	0.018850	-15750.000000	-1213.000000	-4504.000000	-3254.000000	9.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	12.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.505998	5.659614e-01	0.535276	0.08760	0.076300	0.981600	0.755200	0.021100	0.000000	0.137900	0.166700	0.208300	0.048100	0.075600	0.074500	0.000000	0.003600	0.084000	0.074600	0.981600	0.764800	0.019000	0.000000	0.137900	0.166700	0.208300	0.045800	0.077100	0.073100	0.000000	0.001100	0.086400	0.075800	0.981600	0.758500	0.020800	0.000000	0.137900	0.166700	0.208300	0.048700	0.076100	0.074900	0.000000	0.003100	0.068800	0.000000	0.000000	0.000000	0.000000	-757.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	367142.500000	0.000000	1.000000	2.025000e+05	8.086500e+05	34596.000000	6.795000e+05	0.028663	-12413.000000	-289.000000	-2010.000000	-1720.000000	15.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	3.000000	2.000000	2.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.675053	6.636171e-01	0.669057	0.14850	0.112200	0.986600	0.823200	0.051500	0.120000	0.206900	0.333300	0.375000	0.085600	0.121000	0.129900	0.003900	0.027700	0.143900	0.112400	0.986600	0.823600	0.049000	0.120800	0.206900	0.333300	0.375000	0.084100	0.131300	0.125200	0.003900	0.023100	0.148900	0.111600	0.986600	0.825600	0.051300	0.120000	0.206900	0.333300	0.375000	0.086800	0.123100	0.130300	0.003900	0.026600	0.127600	2.000000	0.000000	2.000000	0.000000	-274.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000
max	456255.000000	1.000000	19.000000	1.170000e+08	4.050000e+06	258025.500000	4.050000e+06	0.072508	-7489.000000	365243.000000	0.000000	0.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	20.000000	3.000000	3.000000	23.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.962693	8.549997e-01	0.896010	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	348.000000	34.000000	344.000000	24.000000	0.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000

	SK_ID_CURR	CNT_CHILDREN	AMT_INCOME_TOTAL	AMT_CREDIT	AMT_ANNUITY	AMT_GOODS_PRICE	REGION_POPULATION_RELATIVE	DAYS_BIRTH	DAYS_EMPLOYED	DAYS_REGISTRATION	DAYS_ID_PUBLISH	OWN_CAR_AGE	FLAG_MOBIL	FLAG_EMP_PHONE	FLAG_WORK_PHONE	FLAG_CONT_MOBILE	FLAG_PHONE	FLAG_EMAIL	CNT_FAM_MEMBERS	REGION_RATING_CLIENT	REGION_RATING_CLIENT_W_CITY	HOUR_APPR_PROCESS_START	REG_REGION_NOT_LIVE_REGION	REG_REGION_NOT_WORK_REGION	LIVE_REGION_NOT_WORK_REGION	REG_CITY_NOT_LIVE_CITY	REG_CITY_NOT_WORK_CITY	LIVE_CITY_NOT_WORK_CITY	EXT_SOURCE_1	EXT_SOURCE_2	EXT_SOURCE_3	APARTMENTS_AVG	BASEMENTAREA_AVG	YEARS_BEGINEXPLUATATION_AVG	YEARS_BUILD_AVG	COMMONAREA_AVG	ELEVATORS_AVG	ENTRANCES_AVG	FLOORSMAX_AVG	FLOORSMIN_AVG	LANDAREA_AVG	LIVINGAPARTMENTS_AVG	LIVINGAREA_AVG	NONLIVINGAPARTMENTS_AVG	NONLIVINGAREA_AVG	APARTMENTS_MODE	BASEMENTAREA_MODE	YEARS_BEGINEXPLUATATION_MODE	YEARS_BUILD_MODE	COMMONAREA_MODE	ELEVATORS_MODE	ENTRANCES_MODE	FLOORSMAX_MODE	FLOORSMIN_MODE	LANDAREA_MODE	LIVINGAPARTMENTS_MODE	LIVINGAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAREA_MODE	APARTMENTS_MEDI	BASEMENTAREA_MEDI	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BUILD_MEDI	COMMONAREA_MEDI	ELEVATORS_MEDI	ENTRANCES_MEDI	FLOORSMAX_MEDI	FLOORSMIN_MEDI	LANDAREA_MEDI	LIVINGAPARTMENTS_MEDI	LIVINGAREA_MEDI	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAREA_MEDI	TOTALAREA_MODE	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	DAYS_LAST_PHONE_CHANGE	FLAG_DOCUMENT_2	FLAG_DOCUMENT_3	FLAG_DOCUMENT_4	FLAG_DOCUMENT_5	FLAG_DOCUMENT_6	FLAG_DOCUMENT_7	FLAG_DOCUMENT_8	FLAG_DOCUMENT_9	FLAG_DOCUMENT_10	FLAG_DOCUMENT_11	FLAG_DOCUMENT_12	FLAG_DOCUMENT_13	FLAG_DOCUMENT_14	FLAG_DOCUMENT_15	FLAG_DOCUMENT_16	FLAG_DOCUMENT_17	FLAG_DOCUMENT_18	FLAG_DOCUMENT_19	FLAG_DOCUMENT_20	FLAG_DOCUMENT_21	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR
count	48744.000000	48744.000000	4.874400e+04	4.874400e+04	48720.000000	4.874400e+04	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	16432.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	28212.000000	48736.000000	40076.000000	24857.000000	21103.000000	25888.000000	16926.000000	15249.000000	23555.000000	25165.000000	25423.000000	16278.000000	20490.000000	15964.000000	25192.000000	15397.000000	22660.000000	24857.000000	21103.000000	25888.000000	16926.000000	15249.000000	23555.000000	25165.000000	25423.000000	16278.000000	20490.000000	15964.000000	25192.000000	15397.000000	22660.000000	24857.000000	21103.000000	25888.000000	16926.000000	15249.000000	23555.000000	25165.000000	25423.000000	16278.000000	20490.000000	15964.000000	25192.000000	15397.000000	22660.000000	26120.000000	48715.000000	48715.000000	48715.000000	48715.000000	48744.000000	48744.0	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.000000	48744.0	48744.000000	48744.0	48744.0	48744.0	48744.0	48744.0	48744.0	48744.000000	48744.0	48744.0	48744.0	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000	42695.000000
mean	277796.676350	0.397054	1.784318e+05	5.167404e+05	29426.240209	4.626188e+05	0.021226	-16068.084605	67485.366322	-4967.652716	-3051.712949	11.786027	0.999979	0.809720	0.204702	0.998400	0.263130	0.162646	2.146767	2.038159	2.012596	12.007365	0.018833	0.055166	0.042036	0.077466	0.224664	0.174216	0.501180	0.518021	0.500106	0.122388	0.090065	0.978828	0.751137	0.047624	0.085168	0.151777	0.233706	0.238423	0.067192	0.105885	0.112286	0.009231	0.029387	0.119078	0.088998	0.978292	0.758327	0.045223	0.080570	0.147161	0.229390	0.233854	0.065914	0.110874	0.110687	0.008358	0.028161	0.122809	0.089529	0.978822	0.754344	0.047420	0.084128	0.151200	0.233154	0.237846	0.068069	0.107063	0.113368	0.008979	0.029296	0.107129	1.447644	0.143652	1.435738	0.101139	-1077.766228	0.0	0.786620	0.000103	0.014751	0.087477	0.000041	0.088462	0.004493	0.0	0.001169	0.0	0.0	0.0	0.0	0.0	0.0	0.001559	0.0	0.0	0.0	0.002108	0.001803	0.002787	0.009299	0.546902	1.983769
std	103169.547296	0.709047	1.015226e+05	3.653970e+05	16016.368315	3.367102e+05	0.014428	4325.900393	144348.507136	3552.612035	1569.276709	11.462889	0.004529	0.392526	0.403488	0.039971	0.440337	0.369046	0.890423	0.522694	0.515804	3.278172	0.135937	0.228306	0.200673	0.267332	0.417365	0.379299	0.205142	0.181278	0.189498	0.113112	0.081536	0.049318	0.113188	0.082868	0.139164	0.100669	0.147361	0.164976	0.081909	0.098284	0.114860	0.048749	0.072007	0.113465	0.082655	0.053782	0.110117	0.081169	0.137509	0.101748	0.146485	0.165034	0.082880	0.103980	0.116699	0.046657	0.073504	0.114184	0.081022	0.049663	0.111998	0.082892	0.139014	0.100931	0.147629	0.165241	0.082869	0.099737	0.116503	0.048148	0.072998	0.111420	3.608053	0.514413	3.580125	0.403791	878.920740	0.0	0.409698	0.010128	0.120554	0.282536	0.006405	0.283969	0.066879	0.0	0.034176	0.0	0.0	0.0	0.0	0.0	0.0	0.039456	0.0	0.0	0.0	0.046373	0.046132	0.054037	0.110924	0.693305	1.838873
min	100001.000000	0.000000	2.694150e+04	4.500000e+04	2295.000000	4.500000e+04	0.000253	-25195.000000	-17463.000000	-23722.000000	-6348.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	-1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.013458	0.000008	0.000527	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-4361.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	188557.750000	0.000000	1.125000e+05	2.606400e+05	17973.000000	2.250000e+05	0.010006	-19637.000000	-2910.000000	-7459.250000	-4448.000000	4.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.343695	0.408066	0.363945	0.061900	0.046700	0.976700	0.687200	0.008100	0.000000	0.074500	0.166700	0.104200	0.019000	0.050400	0.048575	0.000000	0.000000	0.058800	0.042500	0.976200	0.692900	0.007600	0.000000	0.069000	0.166700	0.083300	0.016525	0.055100	0.045600	0.000000	0.000000	0.062500	0.046150	0.976700	0.691400	0.008000	0.000000	0.069000	0.166700	0.083300	0.019000	0.051300	0.049000	0.000000	0.000000	0.043200	0.000000	0.000000	0.000000	0.000000	-1766.250000	0.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	277549.000000	0.000000	1.575000e+05	4.500000e+05	26199.000000	3.960000e+05	0.018850	-15785.000000	-1293.000000	-4490.000000	-3234.000000	9.000000	1.000000	1.000000	0.000000	1.000000	0.000000	0.000000	2.000000	2.000000	2.000000	12.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.506771	0.558758	0.519097	0.092800	0.078100	0.981600	0.755200	0.022700	0.000000	0.137900	0.166700	0.208300	0.048300	0.075600	0.077000	0.000000	0.003800	0.085100	0.077000	0.981600	0.758300	0.020300	0.000000	0.137900	0.166700	0.208300	0.046200	0.081700	0.075100	0.000000	0.001200	0.092600	0.077800	0.981600	0.758500	0.022300	0.000000	0.137900	0.166700	0.208300	0.048800	0.077000	0.077600	0.000000	0.003100	0.070700	0.000000	0.000000	0.000000	0.000000	-863.000000	0.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	2.000000
75%	367555.500000	1.000000	2.250000e+05	6.750000e+05	37390.500000	6.300000e+05	0.028663	-12496.000000	-296.000000	-1901.000000	-1706.000000	15.000000	1.000000	1.000000	0.000000	1.000000	1.000000	0.000000	3.000000	2.000000	2.000000	14.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.665956	0.658497	0.652897	0.148500	0.113400	0.986600	0.816400	0.053900	0.160000	0.206900	0.333300	0.375000	0.086800	0.126900	0.137600	0.005100	0.029000	0.150200	0.113550	0.986600	0.823600	0.051700	0.120800	0.206900	0.333300	0.375000	0.085600	0.132200	0.130600	0.003900	0.024500	0.149900	0.113000	0.986600	0.818900	0.053800	0.160000	0.206900	0.333300	0.375000	0.088000	0.126600	0.137425	0.003900	0.028025	0.135700	2.000000	0.000000	2.000000	0.000000	-363.000000	0.0	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	1.000000	3.000000
max	456250.000000	20.000000	4.410000e+06	2.245500e+06	180576.000000	2.245500e+06	0.072508	-7338.000000	365243.000000	0.000000	0.000000	74.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	21.000000	3.000000	3.000000	23.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.939145	0.855000	0.882530	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	354.000000	34.000000	351.000000	24.000000	0.000000	0.0	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.0	1.000000	0.0	0.0	0.0	0.0	0.0	0.0	1.000000	0.0	0.0	0.0	2.000000	2.000000	2.000000	6.000000	7.000000	17.000000

	Percent	Train Missing Count
COMMONAREA_MEDI	69.87	214865
COMMONAREA_AVG	69.87	214865
COMMONAREA_MODE	69.87	214865
NONLIVINGAPARTMENTS_MODE	69.43	213514
NONLIVINGAPARTMENTS_AVG	69.43	213514
NONLIVINGAPARTMENTS_MEDI	69.43	213514
FONDKAPREMONT_MODE	68.39	210295
LIVINGAPARTMENTS_MODE	68.35	210199
LIVINGAPARTMENTS_AVG	68.35	210199
LIVINGAPARTMENTS_MEDI	68.35	210199
FLOORSMIN_AVG	67.85	208642
FLOORSMIN_MODE	67.85	208642
FLOORSMIN_MEDI	67.85	208642
YEARS_BUILD_MEDI	66.50	204488
YEARS_BUILD_MODE	66.50	204488
YEARS_BUILD_AVG	66.50	204488
OWN_CAR_AGE	65.99	202929
LANDAREA_MEDI	59.38	182590
LANDAREA_MODE	59.38	182590
LANDAREA_AVG	59.38	182590

	Percent	Test Missing Count
COMMONAREA_AVG	68.72	33495
COMMONAREA_MODE	68.72	33495
COMMONAREA_MEDI	68.72	33495
NONLIVINGAPARTMENTS_AVG	68.41	33347
NONLIVINGAPARTMENTS_MODE	68.41	33347
NONLIVINGAPARTMENTS_MEDI	68.41	33347
FONDKAPREMONT_MODE	67.28	32797
LIVINGAPARTMENTS_AVG	67.25	32780
LIVINGAPARTMENTS_MODE	67.25	32780
LIVINGAPARTMENTS_MEDI	67.25	32780
FLOORSMIN_MEDI	66.61	32466
FLOORSMIN_AVG	66.61	32466
FLOORSMIN_MODE	66.61	32466
OWN_CAR_AGE	66.29	32312
YEARS_BUILD_AVG	65.28	31818
YEARS_BUILD_MEDI	65.28	31818
YEARS_BUILD_MODE	65.28	31818
LANDAREA_MEDI	57.96	28254
LANDAREA_AVG	57.96	28254
LANDAREA_MODE	57.96	28254

	SK_ID_CURR	SK_ID_BUREAU	CREDIT_ACTIVE	CREDIT_CURRENCY	DAYS_CREDIT	CREDIT_DAY_OVERDUE	DAYS_CREDIT_ENDDATE	DAYS_ENDDATE_FACT	AMT_CREDIT_MAX_OVERDUE	CNT_CREDIT_PROLONG	AMT_CREDIT_SUM	AMT_CREDIT_SUM_DEBT	AMT_CREDIT_SUM_LIMIT	AMT_CREDIT_SUM_OVERDUE	CREDIT_TYPE	DAYS_CREDIT_UPDATE	AMT_ANNUITY
count	1.716428e+06	1.716428e+06	1716428	1716428	1.716428e+06	1.716428e+06	1.610875e+06	1.082775e+06	5.919400e+05	1.716428e+06	1.716415e+06	1.458759e+06	1.124648e+06	1.716428e+06	1716428	1.716428e+06	4.896370e+05
unique	NaN	NaN	4	4	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	15	NaN	NaN
top	NaN	NaN	Closed	currency 1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Consumer credit	NaN	NaN
freq	NaN	NaN	1079273	1715020	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1251615	NaN	NaN
mean	2.782149e+05	5.924434e+06	NaN	NaN	-1.142108e+03	8.181666e-01	5.105174e+02	-1.017437e+03	3.825418e+03	6.410406e-03	3.549946e+05	1.370851e+05	6.229515e+03	3.791276e+01	NaN	-5.937483e+02	1.571276e+04
std	1.029386e+05	5.322657e+05	NaN	NaN	7.951649e+02	3.654443e+01	4.994220e+03	7.140106e+02	2.060316e+05	9.622391e-02	1.149811e+06	6.774011e+05	4.503203e+04	5.937650e+03	NaN	7.207473e+02	3.258269e+05
min	1.000010e+05	5.000000e+06	NaN	NaN	-2.922000e+03	0.000000e+00	-4.206000e+04	-4.202300e+04	0.000000e+00	0.000000e+00	0.000000e+00	-4.705600e+06	-5.864061e+05	0.000000e+00	NaN	-4.194700e+04	0.000000e+00
25%	1.888668e+05	5.463954e+06	NaN	NaN	-1.666000e+03	0.000000e+00	-1.138000e+03	-1.489000e+03	0.000000e+00	0.000000e+00	5.130000e+04	0.000000e+00	0.000000e+00	0.000000e+00	NaN	-9.080000e+02	0.000000e+00
50%	2.780550e+05	5.926304e+06	NaN	NaN	-9.870000e+02	0.000000e+00	-3.300000e+02	-8.970000e+02	0.000000e+00	0.000000e+00	1.255185e+05	0.000000e+00	0.000000e+00	0.000000e+00	NaN	-3.950000e+02	0.000000e+00
75%	3.674260e+05	6.385681e+06	NaN	NaN	-4.740000e+02	0.000000e+00	4.740000e+02	-4.250000e+02	0.000000e+00	0.000000e+00	3.150000e+05	4.015350e+04	0.000000e+00	0.000000e+00	NaN	-3.300000e+01	1.350000e+04
max	4.562550e+05	6.843457e+06	NaN	NaN	0.000000e+00	2.792000e+03	3.119900e+04	0.000000e+00	1.159872e+08	9.000000e+00	5.850000e+08	1.701000e+08	4.705600e+06	3.756681e+06	NaN	3.720000e+02	1.184534e+08

	SK_ID_BUREAU	MONTHS_BALANCE	STATUS
0	5715448	0	C
1	5715448	-1	C
2	5715448	-2	C
3	5715448	-3	C
4	5715448	-4	C

	SK_ID_BUREAU	TARGET	MONTHS_BALANCE	STATUS
0	6158904.0	1	-15.0	C
1	6158904.0	1	-16.0	C
2	6158904.0	1	-17.0	0
3	6158904.0	1	-18.0	0
4	6158904.0	1	-19.0	0

	SK_ID_PREV	SK_ID_CURR	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
count	1.670214e+06	1.670214e+06	1.297979e+06	1.670214e+06	1.670213e+06	7.743700e+05	1.284699e+06	1.670214e+06	1.670214e+06	774370.000000	5951.000000	5951.000000	1.670214e+06	1.670214e+06	1.297984e+06	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000	997149.000000
mean	1.923089e+06	2.783572e+05	1.595512e+04	1.752339e+05	1.961140e+05	6.697402e+03	2.278473e+05	1.248418e+01	9.964675e-01	0.079637	0.188357	0.773503	-8.806797e+02	3.139511e+02	1.605408e+01	342209.855039	13826.269337	33767.774054	76582.403064	81992.343838	0.332570
std	5.325980e+05	1.028148e+05	1.478214e+04	2.927798e+05	3.185746e+05	2.092150e+04	3.153966e+05	3.334028e+00	5.932963e-02	0.107823	0.087671	0.100879	7.790997e+02	7.127443e+03	1.456729e+01	88916.115833	72444.869708	106857.034789	149647.415123	153303.516729	0.471134
min	1.000001e+06	1.000010e+05	0.000000e+00	0.000000e+00	0.000000e+00	-9.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	-0.000015	0.034781	0.373150	-2.922000e+03	-1.000000e+00	0.000000e+00	-2922.000000	-2892.000000	-2801.000000	-2889.000000	-2874.000000	0.000000
25%	1.461857e+06	1.893290e+05	6.321780e+03	1.872000e+04	2.416050e+04	0.000000e+00	5.084100e+04	1.000000e+01	1.000000e+00	0.000000	0.160716	0.715645	-1.300000e+03	-1.000000e+00	6.000000e+00	365243.000000	-1628.000000	-1242.000000	-1314.000000	-1270.000000	0.000000
50%	1.923110e+06	2.787145e+05	1.125000e+04	7.104600e+04	8.054100e+04	1.638000e+03	1.123200e+05	1.200000e+01	1.000000e+00	0.051605	0.189122	0.835095	-5.810000e+02	3.000000e+00	1.200000e+01	365243.000000	-831.000000	-361.000000	-537.000000	-499.000000	0.000000
75%	2.384280e+06	3.675140e+05	2.065842e+04	1.803600e+05	2.164185e+05	7.740000e+03	2.340000e+05	1.500000e+01	1.000000e+00	0.108909	0.193330	0.852537	-2.800000e+02	8.200000e+01	2.400000e+01	365243.000000	-411.000000	129.000000	-74.000000	-44.000000	1.000000
max	2.845382e+06	4.562550e+05	4.180581e+05	6.905160e+06	6.905160e+06	3.060045e+06	6.905160e+06	2.300000e+01	1.000000e+00	1.000000	1.000000	1.000000	-1.000000e+00	4.000000e+06	8.400000e+01	365243.000000	365243.000000	365243.000000	365243.000000	365243.000000	1.000000

	Percent	Missing Count
RATE_INTEREST_PRIMARY	99.67	1425364
RATE_INTEREST_PRIVILEGED	99.67	1425364
AMT_DOWN_PAYMENT	53.56	765994
RATE_DOWN_PAYMENT	53.56	765994
NAME_TYPE_SUITE	49.72	711126
NFLAG_INSURED_ON_APPROVAL	40.38	577560
DAYS_TERMINATION	40.38	577560
DAYS_LAST_DUE	40.38	577560
DAYS_LAST_DUE_1ST_VERSION	40.38	577560
DAYS_FIRST_DUE	40.38	577560
DAYS_FIRST_DRAWING	40.38	577560
AMT_GOODS_PRICE	23.49	335979
AMT_ANNUITY	22.63	323672
CNT_PAYMENT	22.63	323667
PRODUCT_COMBINATION	1.17	16767
AMT_CREDIT	1.15	16455
NAME_YIELD_GROUP	1.15	16454
NAME_GOODS_CATEGORY	1.15	16454
NAME_SELLER_INDUSTRY	1.15	16454
SELLERPLACE_AREA	1.15	16454

	Percent	Missing Count	Total Count without Missing value	Total Count
AMT_ANNUITY	71.47	1226791	489637	1716428
AMT_CREDIT_MAX_OVERDUE	65.51	1124488	591940	1716428
DAYS_ENDDATE_FACT	36.92	633653	1082775	1716428
AMT_CREDIT_SUM_LIMIT	34.48	591780	1124648	1716428
AMT_CREDIT_SUM_DEBT	15.01	257669	1458759	1716428
DAYS_CREDIT_ENDDATE	6.15	105553	1610875	1716428
AMT_CREDIT_SUM	0.00	13	1716415	1716428
CREDIT_ACTIVE	0.00	0	1716428	1716428
CREDIT_CURRENCY	0.00	0	1716428	1716428
DAYS_CREDIT	0.00	0	1716428	1716428
CREDIT_DAY_OVERDUE	0.00	0	1716428	1716428
SK_ID_BUREAU	0.00	0	1716428	1716428
CNT_CREDIT_PROLONG	0.00	0	1716428	1716428
AMT_CREDIT_SUM_OVERDUE	0.00	0	1716428	1716428
CREDIT_TYPE	0.00	0	1716428	1716428
DAYS_CREDIT_UPDATE	0.00	0	1716428	1716428
SK_ID_CURR	0.00	0	1716428	1716428

	SK_ID_BUREAU	MONTHS_BALANCE	STATUS
count	2.729992e+07	2.729992e+07	27299925
unique	NaN	NaN	8
top	NaN	NaN	C
freq	NaN	NaN	13646993
mean	6.036297e+06	-3.074169e+01	NaN
std	4.923489e+05	2.386451e+01	NaN
min	5.001709e+06	-9.600000e+01	NaN
25%	5.730933e+06	-4.600000e+01	NaN
50%	6.070821e+06	-2.500000e+01	NaN
75%	6.431951e+06	-1.100000e+01	NaN
max	6.842888e+06	0.000000e+00	NaN

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	AMT_BALANCE	AMT_CREDIT_LIMIT_ACTUAL	AMT_DRAWINGS_ATM_CURRENT	AMT_DRAWINGS_CURRENT	AMT_DRAWINGS_OTHER_CURRENT	AMT_DRAWINGS_POS_CURRENT	AMT_INST_MIN_REGULARITY	AMT_PAYMENT_CURRENT	AMT_PAYMENT_TOTAL_CURRENT	AMT_RECEIVABLE_PRINCIPAL	AMT_RECIVABLE	AMT_TOTAL_RECEIVABLE	CNT_DRAWINGS_ATM_CURRENT	CNT_DRAWINGS_CURRENT	CNT_DRAWINGS_OTHER_CURRENT	CNT_DRAWINGS_POS_CURRENT	CNT_INSTALMENT_MATURE_CUM	NAME_CONTRACT_STATUS	SK_DPD	SK_DPD_DEF
count	3.840312e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.090496e+06	3.840312e+06	3.090496e+06	3.090496e+06	3.535076e+06	3.072324e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.840312e+06	3.090496e+06	3.840312e+06	3.090496e+06	3.090496e+06	3.535076e+06	3840312	3.840312e+06	3.840312e+06
unique	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7	NaN	NaN
top	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Active	NaN	NaN
freq	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3698436	NaN	NaN
mean	1.904504e+06	2.783242e+05	-3.452192e+01	5.830016e+04	1.538080e+05	5.961325e+03	7.433388e+03	2.881696e+02	2.968805e+03	3.540204e+03	1.028054e+04	7.588857e+03	5.596588e+04	5.808881e+04	5.809829e+04	3.094490e-01	7.031439e-01	4.812496e-03	5.594791e-01	2.082508e+01	NaN	9.283667e+00	3.316220e-01
std	5.364695e+05	1.027045e+05	2.666775e+01	1.063070e+05	1.651457e+05	2.822569e+04	3.384608e+04	8.201989e+03	2.079689e+04	5.600154e+03	3.607808e+04	3.200599e+04	1.025336e+05	1.059654e+05	1.059718e+05	1.100401e+00	3.190347e+00	8.263861e-02	3.240649e+00	2.005149e+01	NaN	9.751570e+01	2.147923e+01
min	1.000018e+06	1.000060e+05	-9.600000e+01	-4.202502e+05	0.000000e+00	-6.827310e+03	-6.211620e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	-4.233058e+05	-4.202502e+05	-4.202502e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	NaN	0.000000e+00	0.000000e+00
25%	1.434385e+06	1.895170e+05	-5.500000e+01	0.000000e+00	4.500000e+04	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.523700e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	4.000000e+00	NaN	0.000000e+00	0.000000e+00
50%	1.897122e+06	2.783960e+05	-2.800000e+01	0.000000e+00	1.125000e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.702700e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.500000e+01	NaN	0.000000e+00	0.000000e+00
75%	2.369328e+06	3.675800e+05	-1.100000e+01	8.904669e+04	1.800000e+05	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	6.633911e+03	9.000000e+03	6.750000e+03	8.535924e+04	8.889949e+04	8.891451e+04	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.200000e+01	NaN	0.000000e+00	0.000000e+00
max	2.843496e+06	4.562500e+05	-1.000000e+00	1.505902e+06	1.350000e+06	2.115000e+06	2.287098e+06	1.529847e+06	2.239274e+06	2.028820e+05	4.289207e+06	4.278316e+06	1.472317e+06	1.493338e+06	1.493338e+06	5.100000e+01	1.650000e+02	1.200000e+01	1.650000e+02	1.200000e+02	NaN	3.260000e+03	3.260000e+03

	Percent	Missing Count	Total Count without Missing value	Total Count
AMT_PAYMENT_CURRENT	20.00	767988	3072324	3840312
AMT_DRAWINGS_ATM_CURRENT	19.52	749816	3090496	3840312
CNT_DRAWINGS_POS_CURRENT	19.52	749816	3090496	3840312
AMT_DRAWINGS_OTHER_CURRENT	19.52	749816	3090496	3840312
AMT_DRAWINGS_POS_CURRENT	19.52	749816	3090496	3840312
CNT_DRAWINGS_OTHER_CURRENT	19.52	749816	3090496	3840312
CNT_DRAWINGS_ATM_CURRENT	19.52	749816	3090496	3840312
CNT_INSTALMENT_MATURE_CUM	7.95	305236	3535076	3840312
AMT_INST_MIN_REGULARITY	7.95	305236	3535076	3840312
SK_ID_PREV	0.00	0	3840312	3840312
AMT_TOTAL_RECEIVABLE	0.00	0	3840312	3840312
SK_DPD	0.00	0	3840312	3840312
NAME_CONTRACT_STATUS	0.00	0	3840312	3840312
CNT_DRAWINGS_CURRENT	0.00	0	3840312	3840312
AMT_PAYMENT_TOTAL_CURRENT	0.00	0	3840312	3840312
AMT_RECIVABLE	0.00	0	3840312	3840312
AMT_RECEIVABLE_PRINCIPAL	0.00	0	3840312	3840312
SK_ID_CURR	0.00	0	3840312	3840312
AMT_DRAWINGS_CURRENT	0.00	0	3840312	3840312
AMT_CREDIT_LIMIT_ACTUAL	0.00	0	3840312	3840312
AMT_BALANCE	0.00	0	3840312	3840312
MONTHS_BALANCE	0.00	0	3840312	3840312
SK_DPD_DEF	0.00	0	3840312	3840312

	SK_ID_PREV	SK_ID_CURR	MONTHS_BALANCE	CNT_INSTALMENT	CNT_INSTALMENT_FUTURE	SK_DPD	SK_DPD_DEF
count	1.000136e+07	1.000136e+07	1.000136e+07	9.975287e+06	9.975271e+06	1.000136e+07	1.000136e+07
mean	1.903217e+06	2.784039e+05	-3.501259e+01	1.708965e+01	1.048384e+01	1.160693e+01	6.544684e-01
std	5.358465e+05	1.027637e+05	2.606657e+01	1.199506e+01	1.110906e+01	1.327140e+02	3.276249e+01
min	1.000001e+06	1.000010e+05	-9.600000e+01	1.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.434405e+06	1.895500e+05	-5.400000e+01	1.000000e+01	3.000000e+00	0.000000e+00	0.000000e+00
50%	1.896565e+06	2.786540e+05	-2.800000e+01	1.200000e+01	7.000000e+00	0.000000e+00	0.000000e+00
75%	2.368963e+06	3.674290e+05	-1.300000e+01	2.400000e+01	1.400000e+01	0.000000e+00	0.000000e+00
max	2.843499e+06	4.562550e+05	-1.000000e+00	9.200000e+01	8.500000e+01	4.231000e+03	3.595000e+03

	Percent	Missing Count
CNT_INSTALMENT_FUTURE	0.47	39945
CNT_INSTALMENT	0.47	39930
SK_ID_PREV	0.21	18067
MONTHS_BALANCE	0.21	18067
NAME_CONTRACT_STATUS	0.21	18067
SK_DPD	0.21	18067
SK_DPD_DEF	0.21	18067
SK_ID_CURR	0.00	0
TARGET	0.00	0

	SK_ID_PREV	SK_ID_CURR	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
count	1.360540e+07	1.360540e+07	1.360540e+07	1.360540e+07	1.360540e+07	1.360250e+07	1.360540e+07	1.360250e+07
mean	1.903365e+06	2.784449e+05	8.566373e-01	1.887090e+01	-1.042270e+03	-1.051114e+03	1.705091e+04	1.723822e+04
std	5.362029e+05	1.027183e+05	1.035216e+00	2.666407e+01	8.009463e+02	8.005859e+02	5.057025e+04	5.473578e+04
min	1.000001e+06	1.000010e+05	0.000000e+00	1.000000e+00	-2.922000e+03	-4.921000e+03	0.000000e+00	0.000000e+00
25%	1.434191e+06	1.896390e+05	0.000000e+00	4.000000e+00	-1.654000e+03	-1.662000e+03	4.226085e+03	3.398265e+03
50%	1.896520e+06	2.786850e+05	1.000000e+00	8.000000e+00	-8.180000e+02	-8.270000e+02	8.884080e+03	8.125515e+03
75%	2.369094e+06	3.675300e+05	1.000000e+00	1.900000e+01	-3.610000e+02	-3.700000e+02	1.671021e+04	1.610842e+04
max	2.843499e+06	4.562550e+05	1.780000e+02	2.770000e+02	-1.000000e+00	-1.000000e+00	3.771488e+06	3.771488e+06

	Percent	Missing Count	Total Count without Missing value	Total Count
DAYS_ENTRY_PAYMENT	0.02	2905	13602496	13605401
AMT_PAYMENT	0.02	2905	13602496	13605401
SK_ID_PREV	0.00	0	13605401	13605401
SK_ID_CURR	0.00	0	13605401	13605401
NUM_INSTALMENT_VERSION	0.00	0	13605401	13605401
NUM_INSTALMENT_NUMBER	0.00	0	13605401	13605401
DAYS_INSTALMENT	0.00	0	13605401	13605401
AMT_INSTALMENT	0.00	0	13605401	13605401

	SK_ID_CURR	TARGET	SK_ID_PREV	NUM_INSTALMENT_VERSION	NUM_INSTALMENT_NUMBER	DAYS_INSTALMENT	DAYS_ENTRY_PAYMENT	AMT_INSTALMENT	AMT_PAYMENT
0	100002	1	1038818.0	1.0	16.0	-115.0	-133.0	9251.775	9251.775
1	100002	1	1038818.0	1.0	8.0	-355.0	-375.0	9251.775	9251.775
2	100002	1	1038818.0	2.0	19.0	-25.0	-49.0	53093.745	53093.745
3	100002	1	1038818.0	1.0	12.0	-235.0	-250.0	9251.775	9251.775
4	100002	1	1038818.0	1.0	9.0	-325.0	-344.0	9251.775	9251.775

	SK_ID_CURR	SK_ID_PREV	AMT_ANNUITY	AMT_APPLICATION	AMT_CREDIT	AMT_DOWN_PAYMENT	AMT_GOODS_PRICE	HOUR_APPR_PROCESS_START	NFLAG_LAST_APPL_IN_DAY	RATE_DOWN_PAYMENT	RATE_INTEREST_PRIMARY	RATE_INTEREST_PRIVILEGED	DAYS_DECISION	SELLERPLACE_AREA	CNT_PAYMENT	DAYS_FIRST_DRAWING	DAYS_FIRST_DUE	DAYS_LAST_DUE_1ST_VERSION	DAYS_LAST_DUE	DAYS_TERMINATION	NFLAG_INSURED_ON_APPROVAL
0	100001	1.369693e+06	3951.000	24835.50	23787.00	2520.0	24835.5	13.000000	1.0	0.104326	NaN	NaN	-1740.0	23.0	8.0	365243.0	-1709.000000	-1499.000000	-1619.000000	-1612.000000	0.000000
1	100002	1.038818e+06	9251.775	179055.00	179055.00	0.0	179055.0	9.000000	1.0	0.000000	NaN	NaN	-606.0	500.0	24.0	365243.0	-565.000000	125.000000	-25.000000	-17.000000	0.000000
2	100003	2.281150e+06	56553.990	435436.50	484191.00	3442.5	435436.5	14.666667	1.0	0.050030	NaN	NaN	-1305.0	533.0	10.0	365243.0	-1274.333333	-1004.333333	-1054.333333	-1047.333333	0.666667
3	100004	1.564014e+06	5357.250	24282.00	20106.00	4860.0	24282.0	5.000000	1.0	0.212008	NaN	NaN	-815.0	30.0	4.0	365243.0	-784.000000	-694.000000	-724.000000	-714.000000	0.000000
4	100005	2.176837e+06	4813.200	22308.75	20076.75	4464.0	44617.5	10.500000	1.0	0.108964	NaN	NaN	-536.0	18.0	12.0	365243.0	-706.000000	-376.000000	-466.000000	-460.000000	0.000000

Home Credit Default Risk (HCDR)¶

Some of the challenges¶

Kaggle API setup¶

Team and Plan updates¶

Dataset and how to download¶

Back ground Home Credit Group¶

Home Credit Group¶

Background on the dataset¶

Data files overview¶

Table sizes¶

Downloading the files via Kaggle API¶

Imports¶

Data files overview¶

Data Dictionary¶

Application train¶

Application test¶

The Other datasets¶

Exploratory Data Analysis¶

Dataset sizes¶

Summary of Application train¶

Observation:¶

Missing data for application train¶

Observation¶

Distribution of the target column¶

Observation¶

Correlation with the target column¶

Observation¶

Loan Repaid Analysis Based on Gender¶

Observation¶

Applicants age and whether they repaid loan or not¶

Observation¶

Applicants Housing Situation¶

Observation¶

Loan repayment based on Car Ownership and Rental Property¶

Observation¶

Repayment of Loan Based on the Loan Type¶

Observation¶

Background of the Applicant¶

Education¶

Observation¶

Organisation¶

Observation¶

Occupation¶

Observation¶

Distribution Plots for AMT columns¶

AMT Credit¶

Observation¶

AMT Annuity¶

AMT Goods Price¶

Observation¶

Summary of Bureau¶

Information about the bureau dataset¶

Statistical summary of the bureau dataset¶

Percentage and count of missing values in each column of the bureau dataset¶

Bar plot to visualize the percentage of missing data for each features of bureau dataset¶

Merging bureau and application_train based on SK_ID_CURR¶

Correlation matrix for merged dataframe¶

Observation:¶

Scatterplot for DAYS_CREDIT and DAYS_ENDDATE_FACT column with respect to the TARGET column¶

Observation:¶

Summary of Bureau Balance¶

Information about the bureau_balance dataset¶

Statistical summary of the bureau_balance dataset¶

Percentage and count of missing values in each column of the bureau_balance dataset¶

Merging bureau_balance and merged bureau/application_train based on SK_ID_BUREAU¶

Correlation matrix for bureau_balance_merged dataframe¶

Observation:¶

Summary of Credit Card Balance¶

Information about the credit_card_balance dataset¶

Statistical summary of the credit_card_balance dataset¶

Percentage and count of missing values in each column of the credit_card_balance dataset¶

Bar plot to visualize the percentage of missing data for each features of credit_card_balance dataset¶

Histogram of the distribution of the AMT_PAYMENT_TOTAL_CURRENT feature¶

Observation:¶

Distribution of AMT_CREDIT_LIMIT_ACTUAL feature¶

Observation:¶

Scatter plot to show the relationship between the AMT_CREDIT_LIMIT_ACTUAL and AMT_BALANCE¶

Observation:¶

Merging credit_card_balance and application_train based on SK_ID_CURR¶

Correlation of TARGET feature with credit_card_balance dataset¶

Joining `previous_application` with `application_x`¶

	Percent	Missing Count	Total Count without Missing value	Total Count
CNT_DRAWINGS_ATM_CURRENT_max	78.73	225559	60924	286483
CNT_DRAWINGS_ATM_CURRENT_mean	78.73	225559	60924	286483
CNT_DRAWINGS_ATM_CURRENT_median	78.73	225559	60924	286483
CNT_DRAWINGS_ATM_CURRENT_min	78.73	225559	60924	286483
MONTHS_BALANCE_median_x	69.86	200129	86354	286483
...	...	...	...	...
REG_CITY_NOT_WORK_CITY	0.00	0	286483	286483
REG_CITY_NOT_LIVE_CITY	0.00	0	286483	286483
LIVE_REGION_NOT_WORK_REGION	0.00	0	286483	286483
REG_REGION_NOT_WORK_REGION	0.00	0	286483	286483
SK_ID_CURR	0.00	0	286483	286483

	Feature	Importance
80	CNT_PAYMENT_min	0.071660
46	EMERGENCYSTATE_MODE	0.054275
70	AMT_REQ_CREDIT_BUREAU_HOUR	0.049927
71	AMT_REQ_CREDIT_BUREAU_DAY	0.048500
35	EXT_SOURCE_3	0.043497
...	...	...
60	FLAG_DOCUMENT_12	0.000000
58	FLAG_DOCUMENT_10	0.000000
57	FLAG_DOCUMENT_9	0.000000
55	FLAG_DOCUMENT_7	0.000000
52	FLAG_DOCUMENT_4	0.000000

	Architecture string	Optimizer	Epochs	Train AUC	Valid Auc	Test AUC	Total experiment time
0	199-relu-32-relu-16-relu-8-sigmoid-1	adam	50	76.2%	67.10000000000001%	73.14872195693694%	487.11 secs
1	199-relu-64-relu-32-relu-16-sigmoid-1	adam	50	62.8%	55.400000000000006%	71.84095564070823%	433.81 secs
2	199-relu-64-relu-32-relu-16-dropout-sigmoid-1	adam	10	56.3%	55.00000000000001%	76.73255313226835%	203.19 secs

	SK_ID_CURR	TARGET
0	100001	0.044793
1	100005	0.122536
2	100013	0.072631
3	100028	0.062897
4	100038	0.984631

True Positive (TP)	The number of rows that are correctly predicted as positive by the model.
True Negative (TN)	The number of rows that are correctly predicted as negative by the model.
False Positive (FP)	The number of rows that are incorrectly predicted as positive by the model.
False Negative (FN)	The number of rows that are incorrectly predicted as negative by the model.

index	exp_name	Train Acc	Valid Acc	Test Acc	Train AUC	Valid AUC	Test AUC	Train time	Test time	Best Params	Description
0	Baseline Logisitc Regression-82 input features	91.32%	91.35%	91.28%	76.09%	76.11%	76.16%	10.06 secs	0.50 secs	{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'auto', 'n_jobs': None, 'penalty': 'l2', 'random_state': None, 'solver': 'lbfgs', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}	Joined Feature Aggregated in input features
1	Baseline Decision Tree-82 input features	100.00%	85.41%	85.33%	100.00%	56.18%	56.81%	34.50 secs	0.46 secs	{'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': None, 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'random_state': 42, 'splitter': 'best'}	Joined Feature Aggregated in input features
2	Baseline Random Forest-82 input features	100.00%	91.91%	91.94%	100.00%	74.77%	74.38%	157.52 secs	3.05 secs	{'bootstrap': True, 'ccp_alpha': 0.0, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'max_features': 'sqrt', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False}	Joined Feature Aggregated in input features
3	Baseline GaussianNB-82 input features	28.80%	28.65%	28.57%	64.62%	64.94%	64.18%	2.54 secs	0.53 secs	{'priors': None, 'var_smoothing': 1e-09}	Joined Feature Aggregated in input features
4	Baseline XGBboost-82 input features	92.77%	91.93%	91.95%	89.09%	77.70%	77.70%	199.03 secs	0.58 secs	{'objective': 'binary:logistic', 'use_label_encoder': None, 'base_score': None, 'booster': None, 'callbacks': None, 'colsample_bylevel': None, 'colsample_bynode': None, 'colsample_bytree': None, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': None, 'feature_types': None, 'gamma': None, 'gpu_id': None, 'grow_policy': None, 'importance_type': None, 'interaction_constraints': None, 'learning_rate': None, 'max_bin': None, 'max_cat_threshold': None, 'max_cat_to_onehot': None, 'max_delta_step': None, 'max_depth': None, 'max_leaves': None, 'min_child_weight': None, 'missing': nan, 'monotone_constraints': None, 'n_estimators': 100, 'n_jobs': None, 'num_parallel_tree': None, 'predictor': None, 'random_state': None, 'reg_alpha': None, 'reg_lambda': None, 'sampling_method': None, 'scale_pos_weight': None, 'subsample': None, 'tree_method': None, 'validate_parameters': None, 'verbosity': None}	Joined Feature Aggregated in input features
5	Tuned Logisitc Regression-82 input features	91.32%	91.34%	91.29%	76.03%	76.09%	76.14%	56.75 secs	0.44 secs	{'C': 0.01, 'penalty': 'l2'}	Joined Feature Aggregated+Hyerparameters
6	Tuned Decision Tree-82 input features	91.33%	91.33%	91.34%	67.35%	67.07%	66.84%	81.10 secs	0.61 secs	{'max_depth': 2, 'min_samples_leaf': 2, 'min_samples_split': 2}	Joined Feature Aggregated+Hyerparameters
7	Tuned Random Forest Trees-82 input features	91.80%	91.76%	91.76%	81.64%	74.13%	73.91%	282.92 secs	0.68 secs	{'max_depth': 10, 'min_samples_leaf': 2, 'n_estimators': 10}	Joined Feature Aggregated+Hyerparameters